Methods and compositions for diagnosing and monitoring transplant rejection

ABSTRACT

Methods of diagnosing or monitoring transplant rejection or cytomegalovirus infection in a patient by detecting the expression level of one or more genes or surrogates derived therefrom in the patient are described. Diagnostic oligonucleotides for diagnosing or monitoring transplant rejection or cytomegalovirus infection and kits or systems containing the same are also described.

RELATED APPLICATIONS

This application is a Continuation of application Ser. No. 12/823,090,filed Jun. 24, 2010, which was Continuation application of applicationSer. No. 12/329,173 (now U.S. Pat. No. 7,829,286), filed Dec. 5, 2008,which is a Divisional of application Ser. No. 10/990,275, filed Nov. 15,2004 (now abandoned), which is a divisional of application Ser. No.10/131,831, filed Apr. 24, 2002 (now U.S. Pat. No. 7,026,121), which isa Continuation-in-part of application Ser. No. 10/006,290, filed Oct.22, 2001 (now abandoned), which claims the benefit of U.S. ProvisionalApplication No. 60/296,764, filed Jun. 8, 2001, all of which are herebyincorporated by reference in their entirety.

SUBMISSION OF SEQUENCE LISTING ON ASCII TEXT FILE

The content of the following submission on ASCII text file isincorporated herein by reference in its entirety: a computer readableform (CRF) of the Sequence Listing (file name: 5066125000103SeqList.txt,date recorded: Jun. 13, 2011, size: 1,818 KB).

FIELD OF THE INVENTION

This invention is in the field of expression profiling. In particular,this invention is in the field of leukocyte expression profiling.

BACKGROUND OF THE INVENTION

Many of the current shortcomings in diagnosis, prognosis, riskstratification and treatment of disease can be approached through theidentification of the molecular mechanisms underlying a disease andthrough the discovery of nucleotide sequences (or sets of nucleotidesequences) whose expression patterns predict the occurrence orprogression of disease states, or predict a patient's response to aparticular therapeutic intervention. In particular, identification ofnucleotide sequences and sets of nucleotide sequences with suchpredictive value from cells and tissues that are readily accessiblewould be extremely valuable. For example, peripheral blood is attainablefrom all patients and can easily be obtained at multiple time points atlow cost. This is a desirable contrast to most other cell and tissuetypes, which are less readily accessible, or accessible only throughinvasive and aversive procedures. In addition, the various cell typespresent in circulating blood are ideal for expression profilingexperiments as the many cell types in the blood specimen can be easilyseparated if desired prior to analysis of gene expression. While bloodprovides a very attractive substrate for the study of diseases usingexpression profiling techniques, and for the development of diagnostictechnologies and the identification of therapeutic targets, the value ofexpression profiling in blood samples rests on the degree to whichchanges in gene expression in these cell types are associated with apredisposition to, and pathogenesis and progression of a disease.

There is an extensive literature supporting the role of leukocytes,e.g., T- and B-lymphocytes, monocytes and granulocytes, includingneutrophils, in a wide range of disease processes, including such broadclasses as cardiovascular diseases, inflammatory, autoimmune andrheumatic diseases, infectious diseases, transplant rejection, cancerand malignancy, and endocrine diseases. For example, amongcardiovascular diseases, such commonly occurring diseases asatherosclerosis, restenosis, transplant vasculopathy and acute coronarysyndromes all demonstrate significant T cell involvement (Smith-Norowitzet al. (1999) Clin Immunol 93:168-175; Jude et al. (1994) Circulation90:1662-8; Belch et al. (1997) Circulation 95:2027-31). These diseasesare now recognized as manifestations of chronic inflammatory disordersresulting from an ongoing response to an injury process in the arterialtree (Ross et al. (1999) Ann Thorac Surg 67:1428-33). Differentialexpression of lymphocyte, monocyte and neutrophil genes and theirproducts has been demonstrated clearly in the literature. Particularlyinteresting are examples of differential expression in circulating cellsof the immune system that demonstrate specificity for a particulardisease, such as arteriosclerosis, as opposed to a generalizedassociation with other inflammatory diseases, or for example, withunstable angina rather than quiescent coronary disease.

A number of individual genes, e.g., CD11b/CD18 (Kassirer et al. (1999)Am Heart J 138:555-9); leukocyte elastase (Amaro et al. (1995) Eur HeartJ 16:615-22; and CD40L (Aukrust et al. (1999) Circulation 100:614-20)demonstrate some degree of sensitivity and specificity as markers ofvarious vascular diseases. In addition, the identification ofdifferentially expressed target and fingerprint genes isolated frompurified populations of monocytes manipulated in various in vitroparadigms has been proposed for the diagnosis and monitoring of a rangeof cardiovascular diseases, see, e.g., U.S. Pat. Nos. 6,048,709;6,087,477; 6,099,823; and 6,124,433 “COMPOSITIONS AND METHODS FOR THETREATMENT AND DIAGNOSIS OF CARDIOVASCULAR DISEASE” to Falb (see also, WO97/30065). Lockhart, in U.S. Pat. No. 6,033,860 “EXPRESSION PROFILES INADULT AND FETAL ORGANS” proposes the use of expression profiles for asubset of identified genes in the identification of tissue samples, andthe monitoring of drug effects.

The accuracy of technologies based on expression profiling for thediagnosis, prognosis, and monitoring of disease would be dramaticallyincreased if numerous differentially expressed nucleotide sequences,each with a measure of specificity for a disease in question, could beidentified and assayed in a concerted manner. In order to achieve thisimproved accuracy, the appropriate sets of nucleotide sequences need tobe identified and validated against numerous samples in combination withrelevant clinical data. The present invention addresses these and otherneeds, and applies to any disease or disease state for whichdifferential regulation of genes, or other nucleotide sequences, ofperipheral blood can be demonstrated.

SUMMARY OF THE INVENTION

The present invention is thus directed to a system for detectingdifferential gene expression. In one format, the system has one or moreisolated DNA molecules wherein each isolated DNA molecule detectsexpression of a gene selected from the group of genes corresponding tothe oligonucleotides depicted in the Sequence Listing. It is understoodthat the DNA sequences and oligonucleotides of the invention may haveslightly different sequences that those identified herein. Such sequencevariations are understood to those of ordinary skill in the art to bevariations in the sequence which do not significantly affect the abilityof the sequences to detect gene expression.

The sequences encompassed by the invention have at least 40-50, 50-60,70-80, 80-85, 85-90, 90-95 or 95-100% sequence identity to the sequencesdisclosed herein. In some embodiments, DNA molecules are less than aboutany of the following lengths (in bases or base pairs): 10,000; 5,000;2500; 2000; 1500; 1250; 1000; 750; 500; 300; 250; 200; 175; 150; 125;100; 75; 50; 25; 10. In some embodiments, DNA molecule is greater thanabout any of the following lengths (in bases or base pairs): 10; 15; 20;25; 30; 40; 50; 60; 75; 100; 125; 150; 175; 200; 250; 300; 350; 400;500; 750; 1000; 2000; 5000; 7500; 10000; 20000; 50000. Alternately, aDNA molecule can be any of a range of sizes having an upper limit of10,000; 5,000; 2500; 2000; 1500; 1250; 1000; 750; 500; 300; 250; 200;175; 150; 125; 100; 75; 50; 25; or 10 and an independently selectedlower limit of 10; 15; 20; 25; 30; 40; 50; 60; 75; 100; 125; 150; 175;200; 250; 300; 350; 400; 500; 750; 1000; 2000; 5000; 7500 wherein thelower limit is less than the upper limit.

The gene expression system may be a candidate library, a diagnosticagent, a diagnostic oligonucleotide set or a diagnostic probe set. TheDNA molecules may be genomic DNA, protein nucleic acid (PNA), cDNA orsynthetic oligonucleotides.

In one format, the gene expression system is immobilized on an array.The array may be a chip array, a plate array, a bead array, a pin array,a membrane array, a solid surface array, a liquid array, anoligonucleotide array, a polynucleotide array, a cDNA array, amicrofilter plate, a membrane or a chip.

The present invention is further directed to a method of diagnosing ormonitoring transplant rejection in a patient, comprising detecting theexpression level of one or more genes or surrogates derived therefrom inthe patient to diagnose or monitor transplant rejection in said patientwherein said one or more genes include a nucleotide sequence selectedfrom SEQ ID NO:4; SEQ ID NO:26; SEQ ID NO:60; SEQ ID NO:130; SEQ IDNO:176; SEQ ID NO:184; SEQ ID NO:261; SEQ ID NO:707; SEQ ID NO:792; SEQID NO:841; SEQ ID NO:1024; SEQ ID NO:1128; SEQ ID NO:1140; SEQ IDNO:1333; SEQ ID NO:1345; SEQ ID NO:1435; SEQ ID NO:1749; SEQ ID NO:1778;SEQ ID NO:1956; SEQ ID NO:2086; SEQ ID NO:2228; SEQ ID NO:2518; SEQ IDNO:2519; SEQ ID NO:2770; SEQ ID NO:2801; SEQ ID NO:3134; SEQ ID NO:3263;SEQ ID NO:3842; SEQ ID NO:4092; SEQ ID NO:4191; SEQ ID NO:4460; SEQ IDNO:4515; SEQ ID NO:5108; SEQ ID NO:5280; SEQ ID NO:5573; SEQ ID NO:5673;SEQ ID NO:5834; SEQ ID NO:6091; SEQ ID NO:6112; SEQ ID NO:6221; SEQ IDNO:6309; SEQ ID NO:6347; SEQ ID NO:6514; SEQ ID NO:6573; SEQ ID NO:7094;SEQ ID NO:7199; SEQ ID NO:7481; SEQ ID NO:7482; SEQ ID NO:7605; SEQ IDNO:8076 and SEQ ID NO:8089.

The present invention is further directed to a method of diagnosing ormonitoring cytomegolovirus infection in a patient, by detecting theexpression level of one or more genes or surrogates derived therefrom inthe patient to diagnose or monitor cytomegolovirus infection in thepatient wherein the genes include a nucleotide sequence selected from:SEQ ID NO:4; SEQ ID NO:26; SEQ ID NO:60; SEQ ID NO:130; SEQ ID NO:176;SEQ ID NO:184; SEQ ID NO:261; SEQ ID NO:707; SEQ ID NO:792; SEQ IDNO:841; SEQ ID NO:1024; SEQ ID NO:1128; SEQ ID NO:1140; SEQ ID NO:1333;SEQ ID NO:1345; SEQ ID NO:1435; SEQ ID NO:1749; SEQ ID NO:1778; SEQ IDNO:1956; SEQ ID NO:2086; SEQ ID NO:2228; SEQ ID NO:2518; SEQ ID NO:2519;SEQ ID NO:2770; SEQ ID NO:2801; SEQ ID NO:3134; SEQ ID NO:3263; SEQ IDNO:3842; SEQ ID NO:4092; SEQ ID NO:4191; SEQ ID NO:4460; SEQ ID NO:4515;SEQ ID NO:5108; SEQ ID NO:5280; SEQ ID NO:5573; SEQ ID NO:5673; SEQ IDNO:5834; SEQ ID NO:6091; SEQ ID NO:6112; SEQ ID NO:6221; SEQ ID NO:6309;SEQ ID NO:6347; SEQ ID NO:6514; SEQ ID NO:6573; SEQ ID NO:7094; SEQ IDNO:7199; SEQ ID NO:7481; SEQ ID NO:7482; SEQ ID NO:7605; SEQ ID NO:8076;SEQ ID NO:8089; SEQ ID NO: 4132; SEQ ID NO:4604; SEQ ID NO:2630; SEQ IDNO:3305; SEQ ID NO:3717; SEQ ID NO:5471; SEQ ID NO:5559; SEQ ID NO:6308;SEQ ID NO:1983; SEQ ID NO:4761; SEQ ID NO:5509; SEQ ID NO:2004; SEQ IDNO:1685; SEQ ID NO:2428; SEQ ID NO:4113; SEQ ID NO:6059; SEQ ID NO:1754and SEQ ID NO:375.

The present invention is further directed to a diagnostic agentcomprising an oligonucleotide wherein the oligonucleotide has anucleotide sequence selected from the Sequence Listing wherein theoligonucleotide detects expression of a gene that is differentiallyexpressed in leukocytes in an individual over time.

The present invention is further directed to a system for detecting geneexpression in leukocytes comprising an isolated DNA molecule wherein theisolated DNA molecule detects expression of a gene wherein the gene isselected from the group of genes corresponding to the oligonucleotidesdepicted in the Sequence Listing and the gene is differentiallyexpressed in the leukocytes in an individual with at least one diseasecriterion for a disease selected from Table 1 as compared to theexpression of the gene in leukocytes in an individual without the atleast one disease criterion.

The present invention is further directed to a gene expression candidatelibrary comprising at least two oligonucleotides wherein theoligonucleotides have a sequence selected from those oligonucleotidesequences listed in Table 2, Table 3, and the Sequence Listing. Table 3encompasses Tables 3A, 3B and 3C. The oligonucleotides of the candidatelibrary may comprise deoxyribonucleic acid (DNA), ribonucleic acid(RNA), protein nucleic acid (PNA), synthetic oligonucleotides, orgenomic DNA.

In one embodiment, the candidate library is immobilized on an array. Thearray may comprises one or more of: a chip array, a plate array, a beadarray, a pin array, a membrane array, a solid surface array, a liquidarray, an oligonucleotide array, a polynucleotide array or a cDNA array,a microtiter plate, a pin array, a bead array, a membrane or a chip.Individual members of the libraries are may be separately immobilized.

The present invention is further directed to a diagnosticoligonucleotide set for a disease having at least two oligonucleotideswherein the oligonucleotides have a sequence selected from thoseoligonucleotide sequences listed in Table 2, Table 3, or the SequenceListing which are differentially expressed in leukocytes genes in anindividual with at least one disease criterion for at least oneleukocyte-related disease as compared to the expression in leukocytes inan individual without the at least one disease criterion, whereinexpression of the two or more genes of the gene expression library iscorrelated with at least one disease criterion.

The present invention is further directed to a diagnosticoligonucleotide set for a disease having at least one oligonucleotidewherein the oligonucleotide has a sequence selected from those sequenceslisted in Table 2, Table 3, or the sequence listing which isdifferentially expressed in leukocytes in an individual with at leastone disease criterion for a disease selected from Table 1 as comparedtoleukocytes in an individual without at least one disease criterion,wherein expression of the at least one gene from the gene expressionlibrary is correlated with at least one disease criterion, wherein thedifferential expression of the at least one gene has not previously beendescribed. In one format, two or more oligonucleotides are utilized.

In the diagnostic oligonucleotide sets of the invention the diseasecriterion may include data selected from patient historic, diagnostic,prognostic, risk prediction, therapeutic progress, and therapeuticoutcome data. This includes lab results, radiology results, pathologyresults such as histology, cytology and the like, physical examinationfindings, and medication lists.

In the diagnostic oligonucleotide sets of the invention the leukocytescomprise peripheral blood leukocytes or leukocytes derived from anon-blood fluid. The non-blood fluid may be selected from colon, sinus,spinal fluid, saliva, lymph fluid, esophagus, small bowel, pancreaticduct, biliary tree, ureter, vagina, cervix uterus and pulmonary lavagefluid.

In the diagnostic oligonucleotide sets of the invention the leukocytesmay include leukocytes derived from urine or a joint biopsy sample orbiopsy of any other tissue or may be T-lymphocytes.

In the diagnostic oligonucleotide sets of the invention the disease maybe selected from cardiac allograft rejection, kidney allograftrejection, liver allograft rejection, atherosclerosis, congestive heartfailure, systemic lupus erythematosis (SLE), rheumatoid arthritis,osteoarthritis, and cytomegalovirus infection.

The diagnostic oligonucleotide sets of the invention may further includeone or more cytomegalovirus (CMV) nucleotide sequences, whereinexpression of the CMV nucleotide sequence is correlated with CMVinfection.

The diagnostic nucleotide sets of the invention may further include oneor more Epstein-Ban virus (EBV) nucleotide sequences, wherein expressionof the one or more EBV nucleotide sequences is correlated with EBVinfection.

In the present invention, expression may be differential expression,wherein the differential expression is one or more of a relativeincrease in expression, a relative decrease in expression, presence ofexpression or absence of expression, presence of disease or absence ofdisease. The differential expression may be RNA expression or proteinexpression. The differential expression may be between two or moresamples from the same patient taken on separate occasions or between twoor more separate patients or between two or more genes relative to eachother.

The present invention is further directed to a diagnostic probe set fora disease where the probes correspond to at least one oligonucleotidewherein the oligonucleotides have a sequence such as those listed inTable 2, Table 3, or the Sequence Listing which is differentiallyexpressed in leukocytes in an individual with at least one diseasecriterion for a disease selected from Table 1 as compared to leukocytesin an individual without the at least one disease criterion, whereinexpression of the oligonucleotide is correlated with at least onedisease criterion, and further wherein the differential expression ofthe at least one nucleotide sequence has not previously been described.

The present invention is further directed to a diagnostic probe setwherein the probes include one or more of probes useful for proteomicsand probes for nucleic acids cDNA, or synthetic oligonucleotides.

The present invention is further directed to an isolated nucleic acidhaving a sequences such as those listed in Table 3B or Table 3C or theSequence Listing.

The present invention is further directed to polypeptides wherein thepolypeptides are encoded by the nucleic acid sequences in Tables 3B, 3Cand the Sequence Listing.

The present invention is further directed to a polynucleotide expressionvector containing the polynucleotide of Tables 3B-3C or the SequenceListing in operative association with a regulatory element whichcontrols expression of the polynucleotide in a host cell. The presentinvention is further directed to host cells transformed with theexpression vectors of the invention. The host cell may be prokaryotic oreukaryotic.

The present invention is further directed to fusion proteins produced bythe host cells of the invention. The present invention is furtherdirected to antibodies directed to the fusion proteins of the invention.The antibodies may be monoclonal or polyclonal antibodies.

The present invention is further directed to kits comprising thediagnostic oligonucleotide sets of the invention. The kits may includeinstructions for use of the kit.

The present invention is further directed to a method of diagnosing adisease by obtaining a leukocyte sample from an individual, hybridizingnucleic acid derived from the leukocyte sample with a diagnosticoligonucleotide set, and comparing the expression of the diagnosticoligonucleotide set with a molecular signature indicative of thepresence or absence of the disease.

The present invention is further directed to a method of detecting geneexpression by a) isolating RNA and b) hybridizing the RNA to isolatedDNA molecules wherein the isolated DNA molecules detect expression of agene wherein the gene corresponds to one of the oligonucleotidesdepicted in the Sequence Listing.

The present invention is further directed to a method of detecting geneexpression by a) isolating RNA; b) converting the RNA to nucleic acidderived from the RNA and c) hybridizing the nucleic acid derived fromthe RNA to isolated DNA molecules wherein the isolated DNA moleculesdetect expression of a gene wherein the gene corresponds to one of theoligonucleotides depicted in the Sequence Listing. In one format, thenucleic acid derived from the RNA is cDNA.

The present invention is further directed to a method of detecting geneexpression by a) isolating RNA; b) converting the RNA to cRNA or aRNAand c) hybridizing the cRNA or aRNA to isolated DNA molecules whereinthe isolated DNA molecules detect expression of a gene corresponding toone of the oligonucleotides depicted in the Sequence Listing.

The present invention is further directed to a method of monitoringprogression of a disease by obtaining a leukocyte sample from anindividual, hybridizing the nucleic acid derived from leukocyte samplewith a diagnostic oligonucleotide set, and comparing the expression ofthe diagnostic oligonucleotide set with a molecular signature indicativeof the presence or absence of disease progression.

The present invention is further directed to a method of monitoring therate of progression of a disease by obtaining a leukocyte sample from anindividual, hybridizing the nucleic acid derived from leukocyte samplewith a diagnostic oligonucleotide set, and comparing the expression ofthe diagnostic oligonucleotide set with a molecular signature indicativeof the presence or absence of disease progression.

The present invention is further directed to a method of predictingtherapeutic outcome by obtaining a leukocyte sample from an individual,hybridizing the nucleic acid derived from leukocyte sample with adiagnostic oligonucleotide set, and comparing the expression of thediagnostic oligonucleotide set with a molecular signature indicative ofthe predicted therapeutic outcome.

The present invention is further directed to a method of determiningprognosis by obtaining a leukocyte sample from an individual,hybridizing the nucleic acid derived from leukocyte sample with adiagnostic oligonucleotide set, and comparing the expression of thediagnostic oligonucleotide set with a molecular signature indicative ofthe prognosis.

The present invention is further directed to a method of predictingdisease complications by obtaining a leukocyte sample from anindividual, hybridizing nucleic acid derived from the leukocyte samplewith a diagnostic oligonucleotide set, and comparing the expression ofthe diagnostic oligonucleotide set with a molecular signature indicativeof the presence or absence of disease complications.

The present invention is further directed to a method of monitoringresponse to treatment, by obtaining a leukocyte sample from anindividual, hybridizing the nucleic acid derived from leukocyte samplewith a diagnostic oligonucleotide set, and comparing the expression ofthe diagnostic oligonucleotide set with a molecular signature indicativeof the presence or absence of response to treatment.

In the methods of the invention the invention may further includecharacterizing the genotype of the individual, and comparing thegenotype of the individual with a diagnostic genotype, wherein thediagnostic genotype is correlated with at least one disease criterion.The genotype may be analyzed by one or more methods selected from thegroup consisting of Southern analysis, RFLP analysis, PCR, singlestranded conformation polymorphism and SNP analysis.

The present invention is further directed to a method of non-invasiveimaging by providing an imaging probe for a nucleotide sequence that isdifferentially expressed in leukocytes from an individual with at leastone disease criterion for at least one leukocyte-implicated diseasewhere leukocytes localize at the site of disease, wherein the expressionof the at least one nucleotide sequence is correlated with the at leastone disease criterion by (a) contacting the probe with a population ofleukocytes; (b) allowing leukocytes to localize to the site of diseaseor injury and (c) detecting an image.

The present invention is further directed to a control RNA for use inexpression profile analysis, where the RNA extracted from the buffy coatsamples is from at least four individuals.

The present invention is further directed to a method of collectingexpression profiles, comprising comparing the expression profile of anindividual with the expression profile of buffy coat control RNA, andanalyzing the profile.

The present invention is further directed to a method of RNA preparationsuitable for diagnostic expression profiling by obtaining a leukocytesample from a subject, adding actinomycin-D to a final concentration of1 ug/ml, adding cycloheximide to a final concentration of 10 ug/ml, andextracting RNA from the leukocyte sample. In the method of RNApreparation of the invention the actinomycin-D and cycloheximide may bepresent in a sample tube to which the leukocyte sample is added. Themethod may further include centrifuging the sample at 4° C. to separatemononuclear cells.

The present invention is further directed to a leukocyte oligonucleotideset including at least two oligonucleotides which are differentiallyexpressed in leukocytes undergoing adhesion to an endothelium relativeto expression in leukocytes not undergoing adhesion to an endothelium,wherein expression of the two oligonucleotides is correlated with the atleast one indicator of adhesion state.

The present invention is further directed to a method of identifying atleast one diagnostic probe set for assessing atherosclerosis by (a)providing a library of candidate oligonucleotides, which candidateoligonucleotides are differentially expressed in leukocytes which areundergoing adhesion to an endothelium relative to their expression inleukocytes that are not undergoing adhesion to an endothelium; (b)assessing expression of two or more oligonucleotides, which two or moreoligonucleotides correspond to components of the library of candidateoligonucleotides, in a subject sample of leukocytes; (c) correlatingexpression of the two or more oligonucleotides with at least onecriterion, which criterion includes one or more indicators of adhesionto an endothelium; and, (d) recording the molecular signature in adatabase.

The present invention is further directed to a method of identifying atleast one diagnostic probe set for assessing atherosclerosis by (a)providing a library of candidate oligonucleotides, which candidateoligonucleotides are differentially expressed in leukocytes which areundergoing adhesion to an endothelium relative to their expression inleukocytes that are not undergoing adhesion to an endothelium; (b)assessing expression of two or more oligonucleotides, which two or moreoligonucleotides correspond to components of the library of candidatenucleotide sequences, in a subject sample of epithelial cells; (c)correlating expression of the two or more nucleotide sequences with atleast one criterion, which criterion comprises one or more indicator ofadhesion to an endothelium; and (d) recording the molecular signature ina database.

The present invention is further directed to methods of leukocyteexpression profiling including methods of analyzing longitudinalclinical and expression data. The rate of change and/or magnitude anddirection of change of gene expression can be correlated with diseasestates and the rate of change of clinical conditions/data and/or themagnitude and direction of changes in clinical data. Correlations may bediscovered by examining these expression or clinical changes that arenot found in the absence of such changes.

The present invention is further directed to methods of leukocyteprofiling for analysis and/or detection of one or more viruses. Thevirus may be CMV, HIV, hepatitis or other viruses. Both viral and humanleukocyte genes can be subjected to expression profiling for thesepurposes.

BRIEF DESCRIPTION OF THE SEQUENCE LISTING

The table below gives a description of the sequence listing. There are8830 entries. The Sequence Listing presents 50mer oligonucleotidesequences derived from human leukocyte, plant and viral genes. These arelisted as SEQ IDs 1-8143. The 50mer sequences and their sources are alsodisplayed in Table 8. Most of these 50mers were designed from sequencesof genes in Tables 2, 3A, B and C, Tables 8, 11-12, 14 and the Sequencelisting.

SEQ IDs 8144-8766 are the cDNA sequences derived from human leukocytesthat were not homologous to UniGene sequences or sequences found indbEST at the time they were searched. Some of these sequences matchhuman genomic sequences and are listed in Tables 3B and C. The remainingclones are putative cDNA sequences that contained less than 50% maskednucleotides when submitted to RepeatMasker, were longer than 147nucleotides, and did not have significant similarity to the UniGeneUnique database, dbEST, the NR nucleotide database of Genbank or theassembled human genome of Genbank.

SEQ IDs 8767-8770, 8828-8830 and 8832 are sequences that appear in thetext and examples (primer, masked sequences, exemplary sequences, etc.).

SEQ IDs 8771-8827 are CMV PCR primers described in Example 17.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: FIG. 1 is a schematic flow chart illustrating a schematicinstruction set for characterization of the nucleotide sequence and/orthe predicted protein sequence of novel nucleotide sequences.

FIG. 2: FIG. 2 depicts the components of an automated RNA preparationmachine.

FIG. 3: FIG. 3 describes kits useful for the practice of the invention.FIG. 3A describes the contents of a kit useful for the discovery ofdiagnostic nucleotide sets using microarrays. FIG. 3B describes thecontents of a kit useful for the application of diagnostic nucleotidesets using microarrays. FIG. 3C describes contents of a kit useful forthe application of diagnostic nucleotide sets using real-time PCR.

FIG. 4 shows the results of six hybridizations on a mini array graphed(n=6 for each column). The error bars are the SEM. This experiment showsthat the average signal from AP prepared RNA is 47% of the averagesignal from GS prepared RNA for both Cy3 and Cy5.

FIG. 5 shows the average background subtracted signal for each of nineleukocyte-specific genes on a mini array. This average is for 3-6 of theabove-described hybridizations for each gene. The error bars are theSEM.

FIG. 6 shows the ratio of Cy3 to Cy5 signal for a number of genes. Afternormalization, this ratio corrects for variability among hybridizationsand allows comparison between experiments done at different times. Theratio is calculated as the Cy3 background subtracted signal divided bythe Cy5 background subtracted signal. Each bar is the average for 3-6hybridizations. The error bars are SEM.

FIG. 7 shows data median Cy3 background subtracted signals for controlRNAs using mini arrays.

FIG. 8 shows data from an array hybridization.

FIG. 9: Cardiac Allograft rejection diagnostic genes.

A. Example of rejection and no-rejection samples expression data for 5marker genes. For each sample, the associated rejection grades are shownas are the expression ratios for 5 differentially expressed genes. Thegenes are identified by the SEQ ID number for the oligonucleotide. Theaverage fold difference between grade 0 and grade 3A samples iscalculated at the bottom.

B. CART classification model. Decision tree for a 3 gene classificationmodel for diagnosis of cardiac rejection. In the first step, expressionof gene 7582 is used to divide the patients to 2 branches. The remainingsamples in each branch are then further divided by one remaining gene.The samples are classified as either rejection or no rejection. 1 norejection sample is misclassified as a rejection sample.

C. Surrogates for the CART classification model. For each of the 3splitter genes in the CART rejection model described in the example, 5top surrogate genes are listed that were identified by the CARTalgorithm.

FIGS. 10A and 10B: Validation of differential expression of a genediscovered using microarrays using real-time PCR

FIG. 10A. The Ct for each patient sample on multiple assays is shownalong with the Ct in the R50 control RNA. Triangles represent −RT(reverse transcriptase) controls.

FIG. 10B. The fold difference between the expression of Granzyme B andan Actin reference is shown for 3 samples from patients with and withoutCMV disease.

FIG. 11: Endpoint testing of PCR primers

Electrophoresis and microfluidics are used to assess the product of genespecific PCR primers.

-   -   A. β-GUS gel image. Lane 3 is the image for primers F178 and        R242. Lanes 2 and 1 correspond to the no-template control and        −RT control, respectively.    -   B. The electropherogram of β-GUS primers F178 and R242, a        graphical representation of Lane 3 from the gel image.    -   C. β-Actin gel image. Lane 3 is the image for primers F75 and        R178. Lanes 2 and 1 correspond to the no-template control and        −RT control, respectively.    -   D. The electropherogram of (3-Actin primers F75 and R178, a        graphical representation of Lave 3 from the gel image.

FIG. 12: PCR Primer efficiency testing. A standard curve of Ct versuslog of the starting RNA amount is shown for 2 genes. FIG. 13A-BValidation of differential expression of Granzyme B in CMV patientsusing Real-time PCR

BRIEF DESCRIPTION OF THE TABLES

Table 1: Table 1 lists diseases or conditions amenable to study byleukocyte profiling.

Table 2: Table 2 describes genes and other nucleotide sequencesidentified using data mining of publicly available publication databasesand nucleotide sequence databases. Corresponding Unigene (build 133)cluster numbers are listed with each gene or other nucleotide sequence.

Table 3A: Table 3A describes differentially expressed nucleotidesequences useful for the prediction of clinical outcomes. This tablecontains 4517 identified cDNAs and cDNA regions of genes that aremembers of a leukocyte candidate library, for use in measuring theexpression of nucleotide sequences that could subsequently be correlatedwith human clinical conditions. The regions of similarity were found bysearching three different databases for pair wise similarity usingblastn. The three databases were UniGene Unique build Mar. 30, 2001,file Hs.seq.uniq.Z; the downloadable database atftp.ncbi.nlm.nih.com/blast/db/est human.Z with date Apr. 8, 2001 whichis a section of Genbank version 122; and the non-redundant section ofGenbank ver 123. The Hs.XXXXX numbers represent UniGene accessionnumbers from the Hs.seq.uniq.Z file of Mar. 30, 2001. The clonesequences are not in the sequence listing.

Table 3B: Table 3B describes Identified Genomic Regions that code fornovel mRNAs. The table contains 591 identified genomic regions that arehighly similar to the cDNA clones. Those regions that are within ˜100 to200 Kb of each other on the same contig are likely to represent exons ofthe same gene. The indicated clone is exemplary of the cDNA clones thatmatch the indicated genomic region. The “number clones” column indicateshow many clones were isolated from the libraries that are similar to theindicated region of the chromosome. The probability number is thelikelihood that region of similarity would occur by chance on a randomsequence. The Accession numbers are from the Mar. 15, 2001 build of thehuman genome. The file date for the downloaded data was Apr. 17, 2001.These sequences may prove useful for the prediction of clinicaloutcomes.

Table 3C: Table 3C describes 48 clones whose sequences align to two ormore non-contiguous sequences on the same assembled human contig ofgenomic sequence. The Accession numbers are from the Mar. 15, 2001 buildof the human genome. The file date for the downloaded data was Apr. 17,2001. The alignments of the clone and the contig are indicated in thetable. The start and stop offset of each matching region is indicated inthe table. The sequence of the clones themselves is included in thesequence listing. The alignments of these clones strongly suggest thatthey are novel nucleotide sequences. Furthermore, no EST or mRNAaligning to the clone was found in the database. These sequences mayprove useful for the prediction of clinical outcomes.

Table 4: Table 4 describes patient groups and diagnostic gene sets

Table 5: Table 5 describes the nucleotide sequence databases used in thesequence analysis described herein.

Table 6: Table 6 describes the algorithms and software packages used forexon and polypeptide prediction used in the sequence analysis describedherein.

Table 7: Table 7 describes the databases and algorithms used for theprotein sequence analysis described herein.

Table 8: Table 8 provides a listing of all oligonucleotides designed forthe arrays and their associated genes. In this table, the sequence ID isgiven which corresponds to the sequence listing. The origin of thesequence for inclusion on the array is noted as coming from one of thecDNA libraries described in example 1, mining from databases asdescribed in examples 2 and 20 or identification from the publishedliterature. The unigene number, genebank accession and GI number arealso given for each sequence when known. These data were obtained fromthe Unigene unique database, build 137. The name of the gene associatedwith the accession number is noted. The strand is noted as −1 or 1,meaning that the probe was designed from the complement of the sequence(−1) or directly from the sequence (1). Finally, the nucleotide sequenceof each probe is also given.

Table 9: Database mining. The Library Browser at the NCBI UniGene website was used to identify genes that are specifically expressed inleukocyte cell populations. The table lists the library name and type,the number of sequences in each library and the number used for thearray.

Table 10: Viral gene for arrays. Viral genomes were used to designoligonucleotides for the microarrays. The accession numbers for theviral genomes used are given, along with the gene name and location ofthe region used for oligonucleotide design.

Table 11A. CMV gene expression markers. This table lists theoligonucleotides and associated genes identified as having value for thediagnosis and monitoring of CMV infection. The first column gives theSEQ ID that corresponds to the oligonucleotide in the sequence listing.The origin of the sequence for inclusion on the array is noted as comingfrom one of the cDNA libraries described in example 1, mining fromdatabases as described in examples 2 and 20 or identification from thepublished literature. The unigene number, genebank accession and GInumber are also given for each sequence when known. The SEQ ID for thesequence listing for the full-length genes corresponding to theaccession numbers in the table are also given (SEQ ID Acc). These datawere obtained from the Unigene unique database, build 149, and theGenbank Version 129. The full length sequences are presented in Table14A. The name of the gene associated with the accession number is noted.The strand is noted as −1 or 1, meaning that the probe was designed fromthe complement of the sequence (−1) or directly from the sequence (1).Next, the nucleotide sequence of each probe is also given. For eachgene, the false detection rate (FDR) from the significance analsysisdescribed in example 17 is given if applicable. WBC is the white bloodcell count. WPT is the number of weeks past transplant.

Table 11B. Primers for PCR. For each of the CMV gene expression markersidentified in Table 11A, 2 sets of PCR primer pairs are shown that werederived by the methods described in example 25. The first column givesthe SEQ ID of the oligonucleotide probe used on the microarrays. Themelting temperature (Tm) for each primer is shown, as is thecorresponding SEQ ID for the primer in the sequence listing.

Table 12A. Cardiac rejection gene expression markers. This table liststhe oligonucleotides and associated genes identified as having value forthe diagnosis and monitoring of cardiac rejection. The first columngives the SEQ ID that corresponds to the oligonucleotide in the sequencelisting. The origin of the sequence for inclusion on the array is notedas coming from one of the cDNA libraries described in example 1, miningfrom databases as described in examples 2 and 20 or identification fromthe published literature. The unigene number, genebank accession and GInumber are also given for each sequence when known. The full lengthsequence for the accession number given is in the sequence listing. TheSEQ ID for the sequence listing for the full-length genes correspondingto the accession numbers in the table are also given (SEQ ID ACC). Thesedata were obtained from the Unigene unique database, build 149, and theGenbank Version 129. The full length sequences are presented in Table14B. The name of the gene associated with the accession number is noted.The strand is noted as −1 or 1, meaning that the probe was designed fromthe complement of the sequence (−1) or directly from the sequence (1).Next, the nucleotide sequence of each probe is given. The remainingcolumns give data from the significance analysis and classificationanalysis that identified each gene as a rejection marker. Data isreported from 2 types of data: static data or referenced data, asdescribed in the example. For each gene, the false detection rate (FDR)from the significance analysis described in example 24 is given ifapplicable. The FDR is given as a %. The final columns report thecriteria for selection of each gene as a rejection marker from eitherthe static or referenced data sets:

-   -   A1: Low FDR (<20) in two independent data sets with SAM    -   A2: Occurred greater than 50% of the time in the top 20 genes        with low FDR (<20) by SAM AND occurred at all in Isolator or        CART analyses    -   B1: Occurred greater than 50% of the time in the top 5 terms        with Isolator    -   B2: Identified among the top 10 based on Isolator term        occurrences    -   C1: A primary splitter in a CART analysis of ˜6000 genes    -   C2: Identified among the top 10 based on CART splitter        occurrences    -   D: Part of a logistic regression model    -   E: Part of a K-nearest neighbor model

Table 12B. Primers for PCR. For each of the rejection gene expressionmarkers identified in Table 12A, 2 sets of PCR primer pairs are shownthat were derived by the methods described in example 25. The firstcolumn gives the SEQ ID of the oligonucleotide probe used on themicroarrays. The melting temperature (Tm) for each primer is shown, asis the corresponding SEQ ID for each primer in the sequence listing.

Table 12C. Surrogates for rejection gene expression markers. For some ofthe rejection marker genes identified in Table 12A, genes are identifiedby the SEQ ID number as surrogates. The surrogates are identified assuch by the CART algorithm or by hierarchical clustering (see text).Surrogates identified by hierarchical clustering are static expressionvalues and those identified by CART are either static or referenced (seeexample 24).

Table 13. Dependent variables for discovery of gene expression markersof cardiac allograft rejection. A stable Grade 0 is a Grade 0 biopsy ina patient who does not experience rejection with the subsequent biopsy.HG or highest grade means that the higher of the biopsy grades from thecentralized and local pathologists was used for a definition of thedependent variable.

Table 14A shows the full length CMV gene sequences referred to in Table11A.

Table 14B shows the full length gene sequences for genes related totransplant rejection referred to in Table 12A.

DETAILED DESCRIPTION OF THE INVENTION Definitions

Unless defined otherwise, all scientific and technical terms areunderstood to have the same meaning as commonly used in the art to whichthey pertain. For the purpose of the present invention, the followingterms are defined below.

In the context of the invention, the term “gene expression system”refers to any system, device or means to detect gene expression andincludes diagnostic agents, candidate libraries, oligonucleotide sets orprobe sets.

The term “monitoring” is used herein to describe the use of gene sets toprovide useful information about an individual or an individual's healthor disease status. “Monitoring” can include, determination of prognosis,risk-stratification, selection of drug therapy, assessment of ongoingdrug therapy, prediction of outcomes, determining response to therapy,diagnosis of a disease or disease complication, following progression ofa disease or providing any information relating to a patients healthstatus.

The term “diagnostic oligonucleotide set” generally refers to a set oftwo or more oligonucleotides that, when evaluated for differentialexpression of their products, collectively yields predictive data. Suchpredictive data typically relates to diagnosis, prognosis, monitoring oftherapeutic outcomes, and the like. In general, the components of adiagnostic oligonucleotide set are distinguished from nucleotidesequences that are evaluated by analysis of the DNA to directlydetermine the genotype of an individual as it correlates with aspecified trait or phenotype, such as a disease, in that it is thepattern of expression of the components of the diagnostic nucleotideset, rather than mutation or polymorphism of the DNA sequence thatprovides predictive value. It will be understood that a particularcomponent (or member) of a diagnostic nucleotide set can, in some cases,also present one or more mutations, or polymorphisms that are amenableto direct genotyping by any of a variety of well known analysis methods,e.g., Southern blotting, RFLP, AFLP, SSCP, SNP, and the like.

A “disease specific target oligonucleotide sequence” is a gene or otheroligonucleotide that encodes a polypeptide, most typically a protein, ora subunit of a multi-subunit protein, that is a therapeutic target for adisease, or group of diseases.

A “candidate library” or a “candidate oligonucleotide library” refers toa collection of oligonucleotide sequences (or gene sequences) that byone or more criteria have an increased probability of being associatedwith a particular disease or group of diseases. The criteria can be, forexample, a differential expression pattern in a disease state or inactivated or resting leukocytes in vitro as reported in the scientificor technical literature, tissue specific expression as reported in asequence database, differential expression in a tissue or cell type ofinterest, or the like. Typically, a candidate library has at least 2members or components; more typically, the library has in excess ofabout 10, or about 100, or about 1000, or even more, members orcomponents.

The term “disease criterion” is used herein to designate an indicator ofa disease, such as a diagnostic factor, a prognostic factor, a factorindicated by a medical or family history, a genetic factor, or asymptom, as well as an overt or confirmed diagnosis of a diseaseassociated with several indicators such as those selected from the abovelist. A disease criterian includes data describing a patient's healthstatus, including retrospective or prospective health data, e.g. in theform of the patient's medical history, laboratory test results,diagnostic test result, clinical events, medications, lists, response(s)to treatment and risk factors, etc.

The terms “molecular signature” or “expression profile” refers to thecollection of expression values for a plurality (e.g., at least 2, butfrequently about 10, about 100, about 1000, or more) of members of acandidate library. In many cases, the molecular signature represents theexpression pattern for all of the nucleotide sequences in a library orarray of candidate or diagnostic nucleotide sequences or genes.Alternatively, the molecular signature represents the expression patternfor one or more subsets of the candidate library. The term“oligonucleotide” refers to two or more nucleotides. Nucleotides may beDNA or RNA, naturally occurring or synthetic.

The term “healthy individual,” as used herein, is relative to aspecified disease or disease criterion. That is, the individual does notexhibit the specified disease criterion or is not diagnosed with thespecified disease. It will be understood, that the individual inquestion, can, of course, exhibit symptoms, or possess various indicatorfactors for another disease.

Similarly, an “individual diagnosed with a disease” refers to anindividual diagnosed with a specified disease (or disease criterion).Such an individual may, or may not, also exhibit a disease criterionassociated with, or be diagnosed with another (related or unrelated)disease.

An “array” is a spatially or logically organized collection, e.g., ofoligonucleotide sequences or nucleotide sequence products such as RNA orproteins encoded by an oligonucleotide sequence. In some embodiments, anarray includes antibodies or other binding reagents specific forproducts of a candidate library.

When referring to a pattern of expression, a “qualitative” difference ingene expression refers to a difference that is not assigned a relativevalue. That is, such a difference is designated by an “all or nothing”valuation. Such an all or nothing variation can be, for example,expression above or below a threshold of detection (an on/off pattern ofexpression). Alternatively, a qualitative difference can refer toexpression of different types of expression products, e.g., differentalleles (e.g., a mutant or polymorphic allele), variants (includingsequence variants as well as post-translationally modified variants),etc.

In contrast, a “quantitative” difference, when referring to a pattern ofgene expression, refers to a difference in expression that can beassigned a value on a graduated scale, (e.g., a 0-5 or 1-10 scale, a+-+++ scale, a grade 1-grade 5 scale, or the like; it will be understoodthat the numbers selected for illustration are entirely arbitrary and inno-way are meant to be interpreted to limit the invention).

Gene Expression Systems of the Invention

The invention is directed to a gene expression system having one or moreDNA molecules wherein the one or more DNA molecules has a nucleotidesequence which detects expression of a gene corresponding to theoligonucleotides depicted in the Sequence Listing. In one format, theoligonucleotide detects expression of a gene that is differentiallyexpressed in leukocytes. The gene expression system may be a candidatelibrary, a diagnostic agent, a diagnostic oligonucleotide set or adiagnostic probe set. The DNA molecules may be genomic DNA, proteinnucleic acid (PNA), cDNA or synthetic oligonucleotides. Following theprocedures taught herein, one can identity sequences of interest foranalyzing gene expression in leukocytes. Such sequences may bepredictive of a disease state.

Diagnostic Oligonucleotides of the Invention

The invention relates to diagnostic nucleotide set(s) comprising membersof the leukocyte candidate library listed in Table 2, Table 3 and in theSequence Listing, for which a correlation exists between the healthstatus of an individual, and the individual's expression of RNA orprotein products corresponding to the nucleotide sequence. In someinstances, only one oligonucleotide is necessary for such detection.Members of a diagnostic oligonucleotide set may be identified by anymeans capable of detecting expression of RNA or protein products,including but not limited to differential expression screening, PCR,RT-PCR, SAGE analysis, high-throughput sequencing, microarrays, liquidor other arrays, protein-based methods (e.g., western blotting,proteomics, and other methods described herein), and data miningmethods, as further described herein.

In one embodiment, a diagnostic oligonucleotide set comprises at leasttwo oligonucleotide sequences listed in Table 2 or Table 3 or theSequence Listing which are differentially expressed in leukocytes in anindividual with at least one disease criterion for at least oneleukocyte-implicated disease relative to the expression in individualwithout the at least one disease criterion, wherein expression of thetwo or more nucleotide sequences is correlated with at least one diseasecriterion, as described below.

In another embodiment, a diagnostic nucleotide set comprises at leastone oligonucleotide having an oligonucleotide sequence listed in Table 2or 3 or the Sequence Listing which is differentially expressed, andfurther wherein the differential expression/correlation has notpreviously been described. In some embodiments, the diagnosticnucleotide set is immobilized on an array.

In another embodiment, diagnostic nucleotides (or nucleotide sets) arerelated to the members of the leukocyte candidate library listed inTable 2, Table 3 and in the Sequence Listing, for which a correlationexists between the health status (or disease criterion) of anindividual. The diagnostic nucleotides are partially or totallycontained in (or derived from) full-length gene sequences (or predictedfull-length gene sequences) for the members of the candidate librarylisted in Table 2, 3, Tables 8, 11-12, 14. This includes sequences fromaccession numbers and unigene numbers from Table 8. Table 8 shows theaccession and unigene number (when known) for each oligonucleotide usedon the 8134 gene leukocyte array described in examples 11-13. In somecases, oligonucleotide sequences are designed from EST or Chromosomalsequences from a public database. In these cases the full-length genesequences may not be known. Full-length sequences in these cases can bepredicted using gene prediction algorithms (examples 4-6). Alternativelythe full-length can be determined by cloning and sequencing thefull-length gene or genes that contain the sequence of interest usingstandard molecular biology approaches described here. The same is truefor olignonucleotides designed from our sequencing of cDNA libraries(see examples 1-4) where the cDNA does not match any sequence in thepublic databases.

The diagnostic nucleotides may also be derived from other genes that arecoexpressed with the correlated sequence or full-length gene. Genes mayshare expression patterns because they are regulated in the samemolecular pathway. Because of the similarity of expression behaviorgenes are identified as surrogates in that they can substitute for adiagnostic gene in a diagnostic gene set. Example 10 demonstrates thediscovery of surrogates from the data and the sequence listing identifyand give the sequence for surrogates for lupus diagnostic genes.

As used herein the term “gene cluster” or “cluster” refers to a group ofgenes related by expression pattern. In other words, a cluster of genesis a group of genes with similar regulation across different conditions,such as graft non-rejection verus graft rejection. The expressionprofile for each gene in a cluster should be correlated with theexpression profile of at least one other gene in that cluster.Correlation may be evaluated using a variety of statistical methods. Asused herein the term “surrogate” refers to a gene with an expressionprofile such that it can substitute for a diagnostic gene in adiagnostic assay. Such genes are often members of the same gene clusteras the diagnostic gene. For each member of a diagnostic gene set, a setof potential surrogates can be identified through identification ofgenes with similar expression patterns as described below.

Many statistical analyses produce a correlation coefficient to describethe relatedness between two gene expression patterns. Patterns may beconsidered correlated if the correlation coefficient is greater than orequal to 0.8. In preferred embodiments, the correlation coefficientshould be greater than 0.85, 0.9 or 0.95. Other statistical methodsproduce a measure of mutual information to describe the relatednessbetween two gene expression patterns. Patterns may be consideredcorrelated if the normalized mutual information value is greater than orequal to 0.7. In preferred embodiments, the normalized mutualinformation value should be greater than 0.8, 0.9 or 0.95. Patterns mayalso be considered similar if they cluster closely upon hierarchicalclustering of gene expression data (Eisen et al. 1998). Similar patternsmay be those genes that are among the 1, 2, 5, 10, 20, 50 or 100 nearestneighbors in a hierarchical clustering or have a similarity score (Eisenet al. 1998) of >0.5, 0.7, 0.8, 0.9, 0.95 or 0.99. Similar patterns mayalso be identified as those genes found to be surrogates in aclassification tree by CART (Breiman et al. 1994). Often, but notalways, members of a gene cluster have similar biological functions inaddition to similar gene expression patterns.

Correlated genes, clusters and surrogates are identified for thediagnostic genes of the invention. These surrogates may be used asdiagnostic genes in an assay instead of, or in addition to, thediagnostic genes for which they are surrogates.

The invention also provides diagnostic probe sets. It is understood thata probe includes any reagent capable of specifically identifying anucleotide sequence of the diagnostic nucleotide set, including but notlimited to amplified DNA, amplified RNA, cDNA, syntheticoligonucleotide, partial or full-length nucleic acid sequences. Inaddition, the probe may identify the protein product of a diagnosticnucleotide sequence, including, for example, antibodies and otheraffinity reagents.

It is also understood that each probe can correspond to one gene, ormultiple probes can correspond to one gene, or both, or one probe cancorrespond to more than one gene.

Homologs and variants of the disclosed nucleic acid molecules may beused in the present invention. Homologs and variants of these nucleicacid molecules will possess a relatively high degree of sequenceidentity when aligned using standard methods. The sequences encompassedby the invention have at least 40-50, 50-60, 70-80, 80-85, 85-90, 90-95or 95-100% sequence identity to the sequences disclosed herein.

It is understood that for expression profiling, variations in thedisclosed sequences will still permit detection of gene expression. Thedegree of sequence identity required to detect gene expression variesdepending on the length of the oligomer. For a 60 mer, 6-8 randommutations or 6-8 random deletions in a 60 mer do not affect geneexpression detection. Hughes, T R, et al. “Expression profiling usingmicroarrays fabricated by an ink-jet oligonucleotide synthesizer. NatureBiotechnology, 19:343-347 (2001). As the length of the DNA sequence isincreased, the number of mutations or deletions permitted while stillallowing gene expression detection is increased.

As will be appreciated by those skilled in the art, the sequences of thepresent invention may contain sequencing errors. That is, there may beincorrect nucleotides, frameshifts, unknown nucleotides, or other typesof sequencing errors in any of the sequences; however, the correctsequences will fall within the homology and stringency definitionsherein.

The minimum length of an oligonucleotide probe necessary for specifichybridization in the human genome can be estimated using two approaches.The first method uses a statistical argument that the probe will beunique in the human genome by chance. Briefly, the number of independentperfect matches (Po) expected for an oligonucleotide of length L in agenome of complexity C can be calculated from the equation (Laird C D,Chromosoma 32:378 (1971):

Po=(¼)^(L)*2C

In the case of mammalian genomes, 2C=˜3.6×10⁹, and an oligonucleotide of14-15 nucleotides is expected to be represented only once in the genome.However, the distribution of nucleotides in the coding sequence ofmammalian genomes is nonrandom (Lathe, R. J. Mol. Biol. 183:1 (1985) andlonger oligonucleotides may be preferred in order to in increase thespecificity of hybridization. In practical terms, this works out toprobes that are 19-40 nucleotides long (Sambrook J et al., infra). Thesecond method for estimating the length of a specific probe is to use aprobe long enough to hybridize under the chosen conditions and use acomputer to search for that sequence or close matches to the sequence inthe human genome and choose a unique match. Probe sequences are chosenbased on the desired hybridization properties as described in Chapter 11of Sambrook et al, infra. The PRIMER3 program is useful for designingthese probes (S. Rozen and H. Skaletsky 1996, 1997; Primer3 codeavailable at the web site located atgenome.wi.mitedu/genome_software/other/primer3.html). The sequences ofthese probes are then compared pair wise against a database of the humangenome sequences using a program such as BLAST or MEGABLAST (Madden, T.L et al. (1996) Meth. Enzymol. 266:131-141). Since most of the humangenome is now contained in the database, the number of matches will bedetermined. Probe sequences are chosen that are unique to the desiredtarget sequence.

In some embodiments, a diagnostic probe set is immobilized on an array.The array is optionally comprises one or more of: a chip array, a platearray, a bead array, a pin array, a membrane array, a solid surfacearray, a liquid array, an oligonucleotide array, a polynucleotide arrayor a cDNA array, a microtiter plate, a pin array, a bead array, amembrane or a chip.

In some embodiments, the leukocyte-implicated disease is selected fromthe diseases listed in Table 1. In other embodiments, the disease isatherosclerosis or cardiac allograft rejection. In other embodiments,the disease is congestive heart failure, angina, myocardial infarction,systemic lupus erythematosis (SLE) and rheumatoid arthritis.

In some embodiments, diagnostic nucleotides of the invention are used asa diagnostic gene set in combination with genes that are know to beassociated with a disease state (“known markers”). The use of thediagnostic nucleotides in combination with the known markers can provideinformation that is not obtainable through the known markers alone. Theknown markers include those identified by the prior art listingprovided.

General Molecular Biology References

In the context of the invention, nucleic acids and/or proteins aremanipulated according to well known molecular biology techniques.Detailed protocols for numerous such procedures are described in, e.g.,in Ausubel et al. Current Protocols in Molecular Biology (supplementedthrough 2000) John Wiley & Sons, New York (“Ausubel”); Sambrook et al.Molecular Cloning—A Laboratory Manual (2nd Ed.), Vol. 1-3, Cold SpringHarbor Laboratory, Cold Spring Harbor, N.Y., 1989 (“Sambrook”), andBerger and Kimmel Guide to Molecular Cloning Techniques, Methods inEnzymology volume 152 Academic Press, Inc., San Diego, Calif.(“Berger”).

In addition to the above references, protocols for in vitroamplification techniques, such as the polymerase chain reaction (PCR),the ligase chain reaction (LCR), Q-replicase amplification, and otherRNA polymerase mediated techniques (e.g., NASBA), useful e.g., foramplifying cDNA probes of the invention, are found in Mullis et al.(1987) U.S. Pat. No. 4,683,202; PCR Protocols A Guide to Methods andApplications (Innis et al. eds) Academic Press Inc. San Diego, Calif.(1990) (“Innis”); Arnheim and Levinson (1990) C&EN 36; The Journal OfNIH Research (1991) 3:81; Kwoh et al. (1989) Proc Natl Acad Sci USA 86,1173; Guatelli et al. (1990) Proc Natl Acad Sci USA 87:1874; Lomell etal. (1989) J Clin Chem 35:1826; Landegren et al. (1988) Science241:1077; Van Brunt (1990) Biotechnology 8:291; Wu and Wallace (1989)Gene 4: 560; Barringer et al. (1990) Gene 89:117, and Sooknanan andMalek (1995) Biotechnology 13:563. Additional methods, useful forcloning nucleic acids in the context of the present invention, includeWallace et al. U.S. Pat. No. 5,426,039. Improved methods of amplifyinglarge nucleic acids by PCR are summarized in Cheng et al. (1994) Nature369:684 and the references therein.

Certain polynucleotides of the invention, e.g., oligonucleotides can besynthesized utilizing various solid-phase strategies involvingmononucleotide- and/or trinucleotide-based phosphoramidite couplingchemistry. For example, nucleic acid sequences can be synthesized by thesequential addition of activated monomers and/or trimers to anelongating polynucleotide chain. See e.g., Caruthers, M. H. et al.(1992) Meth Enzymol 211:3.

In lieu of synthesizing the desired sequences, essentially any nucleicacid can be custom ordered from any of a variety of commercial sources,such as The Midland Certified Reagent Company (mcrc@oligos.com), TheGreat American Gene Company ExpressGen, Inc., Operon Technologies, Inc.and many others.

Similarly, commercial sources for nucleic acid and protein microarraysare available, and include, e.g., Agilent Technologies, Palo Alto,Calif. Affymetrix, Santa Clara, Calif.; and others.

Identification of Diagnostic Nucleotide Sets

Candidate Library

Libraries of candidates that are differentially expressed in leukocytesare substrates for the identification and evaluation of diagnosticoligonucleotide sets and disease specific target nucleotide sequences.

The term leukocyte is used generically to refer to any nucleated bloodcell that is not a nucleated erythrocyte. More specifically, leukocytescan be subdivided into two broad classes. The first class includesgranulocytes, including, most prevalently, neutrophils, as well aseosinophils and basophils at low frequency. The second class, thenon-granular or mononuclear leukocytes, includes monocytes andlymphocytes (e.g., T cells and B cells). There is an extensiveliterature in the art implicating leukocytes, e.g., neutrophils,monocytes and lymphocytes in a wide variety of disease processes,including inflammatory and rheumatic diseases, neurodegenerativediseases (such as Alzheimer's dementia), cardiovascular disease,endocrine diseases, transplant rejection, malignancy and infectiousdiseases, and other diseases listed in Table 1. Mononuclear cells areinvolved in the chronic immune response, while granulocytes, which makeup approximately 60% of the leukocytes, have a non-specific andstereotyped response to acute inflammatory stimuli and often have a lifespan of only 24 hours.

In addition to their widespread involvement and/or implication innumerous disease related processes, leukocytes are particularlyattractive substrates for clinical and experimental evaluation for avariety of reasons. Most importantly, they are readily accessible at lowcost from essentially every potential subject. Collection is minimallyinvasive and associated with little pain, disability or recovery time.Collection can be performed by minimally trained personnel (e.g.,phlebotomists, medical technicians, etc.) in a variety of clinical andnon-clinical settings without significant technological expenditure.Additionally, leukocytes are renewable, and thus available at multipletime points for a single subject.

Assembly of Candidate Libraries

At least two conceptually distinct approaches to the assembly ofcandidate libraries exist. Either, or both, or other, approaches can befavorably employed. The method of assembling, or identifying, candidatelibraries is secondary to the criteria utilized for selectingappropriate library members. Most importantly, library members areassembled based on differential expression of RNA or protein products inleukocyte populations. More specifically, candidate nucleotide sequencesare induced or suppressed, or expressed at increased or decreased levelsin leukocytes from a subject with one or more disease or disease state(a disease criterion) relative to leukocytes from a subject lacking thespecified disease criterion. Alternatively, or in addition, librarymembers can be assembled from among nucleotide sequences that aredifferentially expressed in activated or resting leukocytes relative toother cell types.

Firstly, publication and sequence databases can be “mined” using avariety of search strategies, including, e.g., a variety of genomics andproteomics approaches. For example, currently available scientific andmedical publication databases such as Medline, Current Contents, OMIM(online Mendelian inheritance in man) various Biological and ChemicalAbstracts, Journal indexes, and the like can be searched using term orkey-word searches, or by author, title, or other relevant searchparameters. Many such databases are publicly available, and one of skillis well versed in strategies and procedures for identifying publicationsand their contents, e.g., genes, other nucleotide sequences,descriptions, indications, expression pattern, etc. Numerous databasesare available through the internet for free or by subscription, see,e.g., at the web sites located at ncbi.nlm.nih.gov/PubMed/;3.infotrieve.com/; isinet.com/; sciencemag.org/. Additional oralternative publication or citation databases are also available thatprovide identical or similar types of information, any of which arefavorable employed in the context of the invention. These databases canbe searched for publications describing differential gene expression inleukocytes between patient with and without diseases or conditionslisted in Table 1. We identified the nucleotide sequences listed inTable 2 and some of the sequences listed in Table 8 (Example 20), usingdata mining methods.

Alternatively, a variety of publicly available and proprietary sequencedatabases (including GenBank, dbEST, UniGene, and TIGR and SAGEdatabases) including sequences corresponding to expressed nucleotidesequences, such as expressed sequence tags (ESTs) are available. Forexample, Genbank™ ncbi.nlm.nih.gov/Genbank/) among others can be readilyaccessed and searched via the internet. These and other sequence andclone database resources are currently available; however, any number ofadditional or alternative databases comprising nucleotide sequencesequences, EST sequences, clone repositories, PCR primer sequences, andthe like corresponding to individual nucleotide sequence sequences arealso suitable for the purposes of the invention. Sequences fromnucleotide sequences can be identified that are only found in librariesderived from leukocytes or sub-populations of leukocytes, for examplesee Table 2.

Alternatively, the representation, or relative frequency, of anucleotide sequence may be determined in a leukocyte-derived nucleicacid library and compared to the representation of the sequence innon-leukocyte derived libraries. The representation of a nucleotidesequence correlates with the relative expression level of the nucleotidesequence in leukocytes and non-leukocytes. An oligonucleotide sequencewhich has increased or decreased representation in a leukocyte-derivednucleic acid library relative to a non-leukocyte-derived libraries is acandidate for a leukocyte-specific gene.

Nucleotide sequences identified as having specificity to activated orresting leukocytes or to leukocytes from patients or patient sampleswith a variety of disease types can be isolated for use in a candidatelibrary for leukocyte expression profiling through a variety ofmechanisms. These include, but are not limited to, the amplification ofthe nucleotide sequence from RNA or DNA using nucleotide sequencespecific primers for PCR or RT-PCR, isolation of the nucleotide sequenceusing conventional cloning methods, the purchase of an IMAGE consortiumcDNA clone (EST) with complimentary sequence or from the same expressednucleotide sequence, design of oligonucleotides, preparation ofsynthetic nucleic acid sequence, or any other nucleic-acid based method.In addition, the protein product of the nucleotide sequence can beisolated or prepared, and represented in a candidate library, usingstandard methods in the art, as described further below.

While the above discussion related primarily to “genomics” approaches,it is appreciated that numerous, analogous “proteomics” approaches aresuitable to the present invention. For example, a differentiallyexpressed protein product can, for example, be detected using westernanalysis, two-dimensional gel analysis, chromatographic separation, massspectrometric detection, protein-fusion reporter constructs,colorometric assays, binding to a protein array, or by characterizationof polysomal mRNA. The protein is further characterized and thenucleotide sequence encoding the protein is identified using standardtechniques, e.g. by screening a cDNA library using a probe based onprotein sequence information.

The second approach involves the construction of a differentialexpression library by any of a variety of means. Any one or more ofdifferential screening, differential display or subtractivehybridization procedures, or other techniques that preferentiallyidentify, isolate or amplify differentially expressed nucleotidesequences can be employed to produce a library of differentiallyexpressed candidate nucleotide sequences, a subset of such a library, apartial library, or the like. Such methods are well known in the art.For example, peripheral blood leukocytes, (i.e., a mixed populationincluding lymphocytes, monocytes and neutrophils), from multiple donorsamples are pooled to prevent bias due to a single-donor's uniquegenotype. The pooled leukocytes are cultured in standard medium andstimulated with individual cytokines or growth factors e.g., with IL-2,IL-1, MCP1, TNFα, and/or IL8 according to well known procedures (see,e.g., Tough et al. (1999); Winston et al. (1999); Hansson et al.(1989)). Typically, leukocytes are recovered from Buffy coatpreparations produced by centrifugation of whole blood. Alternatively,mononuclear cells (monocytes and lymphocytes) can be obtained by densitygradient centrifugation of whole blood, or specific cell types (such asa T lymphocyte) can be isolated using affinity reagents to cell specificsurface markers. Leukocytes may also be stimulated by incubation withionomycin, and phorbol myristate acetate (PMA). This stimulationprotocol is intended to non-specifically mimic “activation” of numerouspathways due to variety of disease conditions rather than to simulateany single disease condition or paradigm.

Using well known subtractive hybridization procedures (as described in,e.g., U.S. Pat. Nos. 5,958,738; 5589,339; 5,827,658; 5,712,127;5,643,761) each of which are hereby incorporated by reference, a libraryis produced that is enriched for RNA species (messages) that aredifferentially expressed between test and control leukocyte populations.In some embodiments, the test population of leukocytes are simplystimulated as described above to emulate non-specific activation events,while in other embodiments the test population can be selected fromsubjects (or patients) with a specified disease or class of diseases.Typically, the control leukocyte population lacks the defining testcondition, e.g., stimulation, disease state, diagnosis, genotype, etc.Alternatively, the total RNA from control and test leukocyte populationsare prepared by established techniques, treated with DNAseI, andselected for messenger RNA with an intact 3′ end (i.e., polyA(+)messenger RNA) e.g., using commercially available kits according to themanufacturer's instructions e.g. Clontech. Double stranded cDNA issynthesized utilizing reverse transcriptase. Double stranded cDNA isthen cut with a first restriction enzyme (e.g., NlaIII, that cuts at therecognition site: CATG, and cuts the cDNA sequence at approximately 256bp intervals) that cuts the cDNA molecules into conveniently sizedfragments.

The cDNAs prepared from the test population of leukocytes are dividedinto (typically 2) “tester” pools, while cDNAs prepared from the controlpopulation of leukocytes are designated the “driver” pool. Typically,pooled populations of cells from multiple individual donors are utilizedand in the case of stimulated versus unstimulated cells, thecorresponding tester and driver pools for any single subtractionreaction are derived from the same donor pool.

A unique double-stranded adapter is ligated to each of the tester cDNApopulations using unphosphorylated primers so that only the sense strandis covalently linked to the adapter. An initial hybridization isperformed consisting of each of the tester pools of cDNA (each with itscorresponding adapter) and an excess of the driver cDNA. Typically, anexcess of about 10-100 fold driver relative to tester is employed,although significantly lower or higher ratios can be empiricallydetermined to provide more favorable results. The initial hybridizationresults in an initial normalization of the cDNAs such that high and lowabundance messages become more equally represented followinghybridization due to a failure of driver/tester hybrids to amplify.

A second hybridization involves pooling un-hybridized sequences frominitial hybridizations together with the addition of supplemental drivercDNA. In this step, the expressed sequences enriched in the two testerpools following the initial hybridization can hybridize. Hybridsresulting from the hybridization between members of each of the twotester pools are then recovered by amplification in a polymerase chainreaction (PCR) using primers specific for the unique adapters. Again,sequences originating in a tester pool that form hybrids with componentsof the driver pool are not amplified. Hybrids resulting between membersof the same tester pool are eliminated by the formation of “panhandles”between their common 5′ and 3′ ends. For additional details, see, e.g.,Lukyanov et al. (1997) Biochem Biophys Res Commun 230:285-8.

Typically, the tester and driver pools are designated in thealternative, such that the hybridization is performed in both directionsto ensure recovery of messenger RNAs that are differentially expressedin either a positive or negative manner (i.e., that are turned on orturned off, up-regulated or down-regulated). Accordingly, it will beunderstood that the designation of test and control populations is tosome extent arbitrary, and that a test population can just as easily becompared to leukocytes derived from a patient with the same of anotherdisease of interest.

If so desired, the efficacy of the process can be assessed by suchtechniques as semi-quantitative PCR of known (i.e., control) nucleotidesequences, of varying abundance such as β-actin. The resulting PCRproducts representing partial cDNAs of differentially expressednucleotide sequences are then cloned (i.e., ligated) into an appropriatevector (e.g., a commercially available TA cloning vector, such as pGEMfrom Promega) and, optionally, transformed into competent bacteria forselection and screening.

Either of the above approaches, or both in combination, or indeed, anyprocedure, which permits the assembly of a collection of nucleotidesequences that are expressed in leukocytes, is favorably employed toproduce the libraries of candidates useful for the identification ofdiagnostic nucleotide sets and disease specific target nucleotides ofthe invention. Additionally, any method that permits the assembly of acollection of nucleotides that are expressed in leukocytes andpreferentially associated with one or more disease or condition, whetheror not the nucleotide sequences are differentially expressed, isfavorably employed in the context of the invention. Typically, librariesof about 2,000-10,000 members are produced (although libraries in excessof 10,000 are not uncommon). Following additional evaluation procedures,as described below, the proportion of unique clones in the candidatelibrary can approximate 100%.

A candidate oligonucleotide sequence may be represented in a candidatelibrary by a full-length or partial nucleic acid sequence,deoxyribonucleic acid (DNA) sequence, cDNA sequence, RNA sequence,synthetic oligonucleotides, etc. The nucleic acid sequence can be atleast 19 nucleotides in length, at least 25 nucleotides, at least 40nucleotides, at least 100 nucleotides, or larger. Alternatively, theprotein product of a candidate nucleotide sequence may be represented ina candidate library using standard methods, as further described below.

Characterization of Candidate Oligonucleotide Sequences

The sequence of individual members (e.g., clones, partial sequencelisting in a database such as an EST, etc.) of the candidateoligonucleotide libraries is then determined by conventional sequencingmethods well known in the art, e.g., by the dideoxy-chain terminationmethod of Sanger et al. (1977) Proc Natl Acad Sci USA 74:5463-7; bychemical procedures, e.g., Maxam and Gilbert (1977) Proc Natl Acad SciUSA 74:560-4; or by polymerase chain reaction cycle sequencing methods,e.g., Olsen and Eckstein (1989) Nuc Acid Res 17:9613-20, DNA chip basedsequencing techniques or variations, including automated variations(e.g., as described in Hunkapiller et al. (1991) Science 254:59-67;Pease et al. (1994) Proc Natl Acad Sci USA 91:5022-6), thereof. Numerouskits for performing the above procedures are commercially available andwell known to those of skill in the art. Character strings correspondingto the resulting nucleotide sequences are then recorded (i.e., stored)in a database. Most commonly the character strings are recorded on acomputer readable medium for processing by a computational device.

Generally, to facilitate subsequent analysis, a custom algorithm isemployed to query existing databases in an ongoing fashion, to determinethe identity, expression pattern and potential function of theparticular members of a candidate library. The sequence is firstprocessed, by removing low quality sequence. Next the vector sequencesare identified and removed and sequence repeats are identified andmasked. The remaining sequence is then used in a Blast algorithm againstmultiple publicly available, and/or proprietary databases, e.g., NCBInucleotide, EST and protein databases, Unigene, and Human GenomeSequence. Sequences are also compared to all previously sequencedmembers of the candidate libraries to detect redundancy.

In some cases, sequences are of high quality, but do not match anysequence in the NCBI nr, human EST or Unigene databases. In this casethe sequence is queried against the human genomic sequence. If a singlechromosomal site is matched with a high degree of confidence, thatregion of genomic DNA is identified and subjected to further analysiswith a gene prediction program such as GRAIL. This analysis may lead tothe identification of a new gene in the genomic sequence. This sequencecan then be translated to identify the protein sequence that is encodedand that sequence can be further analyzed using tools such as Pfam,Blast P, or other protein structure prediction programs, as illustratedin Table 7. Typically, the above analysis is directed towards theidentification of putative coding regions, e.g., previously unidentifiedopen reading frames, confirming the presence of known coding sequences,and determining structural motifs or sequence similarities of thepredicted protein (i.e., the conceptual translation product) in relationto known sequences. In addition, it has become increasingly possible toassemble “virtual cDNAs” containing large portions of coding region,simply through the assembly of available expressed sequence tags (ESTs).In turn, these extended nucleic acid and amino acid sequences allow therapid expansion of substrate sequences for homology searches andstructural and functional motif characterization. The results of theseanalysis permits the categorization of sequences according to structuralcharacteristics, e.g., as structural proteins, proteins involved insignal transduction, cell surface or secreted proteins etc.

It is understood that full-length nucleotide sequences may also beidentified using conventional methods, for example, library screening,RT-PCR, chromosome walking, etc., as described in Sambrook and Ausubel,infra.

Candidate Nucleotide Library of the Invention

We identified members of a candidate nucleotide library that aredifferentially expressed in activated leukocytes and resting leukocytes.Accordingly, the invention provides the candidate leukocyte nucleotidelibrary comprising the nucleotide sequences listed in Table 2, Table 3and in the sequence listing. In another embodiment, the inventionprovides a candidate library comprising at least two nucleotidesequences listed in Table 2, Table 3, Tables 8, 11-12, 14 and thesequence listing. In another embodiment, the at least two nucleotidesequence are at least 19 nucleotides in length, at least 35 nucleotides,at least 40 nucleotides or at least 100 nucleotides. In someembodiments, the nucleotide sequences comprises deoxyribonucleic acid(DNA) sequence, ribonucleic acid (RNA) sequence, syntheticoligonucleotide sequence, or genomic DNA sequence. It is understood thatthe nucleotide sequences may each correspond to one gene, or thatseveral nucleotide sequences may correspond to one gene, or both.

The invention also provides probes to the candidate nucleotide library.In one embodiment of the invention, the probes comprise at least twonucleotide sequences listed in Table 2, Table 3, or the sequence listingwhich are differentially expressed in leukocytes in an individual with aleast one disease criterion for at least one leukocyte-related diseaseand in leukocytes in an individual without the at least one diseasecriterion, wherein expression of the two or more nucleotide sequences iscorrelated with at least one disease criterion. It is understood that aprobe may detect either the RNA expression or protein product expressionof the candidate nucleotide library. Alternatively, or in addition, aprobe can detect a genotype associated with a candidate nucleotidesequence, as further described below. In another embodiment, the probesfor the candidate nucleotide library are immobilized on an array.

The candidate nucleotide library of the invention is useful inidentifying diagnostic nucleotide sets of the invention, as describedbelow. The candidate nucleotide sequences may be further characterized,and may be identified as a disease target nucleotide sequence and/or anovel nucleotide sequence, as described below. The candidate nucleotidesequences may also be suitable for use as imaging reagents, as describedbelow.

Generation of Expression Patterns

RNA, DNA or Protein Sample Procurement

Following identification or assembly of a library of differentiallyexpressed candidate nucleotide sequences, leukocyte expression profilescorresponding to multiple members of the candidate library are obtained.Leukocyte samples from one or more subjects are obtained by standardmethods. Most typically, these methods involve trans-cutaneous venoussampling of peripheral blood. While sampling of circulating leukocytesfrom whole blood from the peripheral vasculature is generally thesimplest, least invasive, and lowest cost alternative, it will beappreciated that numerous alternative sampling procedures exist, and arefavorably employed in some circumstances. No pertinent distinctionexists, in fact, between leukocytes sampled from the peripheralvasculature, and those obtained, e.g., from a central line, from acentral artery, or indeed from a cardiac catheter, or during a surgicalprocedure which accesses the central vasculature. In addition, otherbody fluids and tissues that are, at least in part, composed ofleukocytes are also desirable leukocyte samples. For example, fluidsamples obtained from the lung during bronchoscopy may be rich inleukocytes, and amenable to expression profiling in the context of theinvention, e.g., for the diagnosis, prognosis, or monitoring of lungtransplant rejection, inflammatory lung diseases or infectious lungdisease. Fluid samples from other tissues, e.g., obtained by endoscopyof the colon, sinuses, esophagus, stomach, small bowel, pancreatic duct,biliary tree, bladder, ureter, vagina, cervix or uterus, etc., are alsosuitable. Samples may also be obtained other sources containingleukocytes, e.g., from urine, bile, cerebrospinal fluid, feces, gastricor intestinal secretions, semen, or solid organ or joint biopsies.

Most frequently, mixed populations of leukocytes, such as are found inwhole blood are utilized in the methods of the present invention. Acrude separation, e.g., of mixed leukocytes from red blood cells, and/orconcentration, e.g., over a sucrose, percoll or ficoll gradient, or byother methods known in the art, can be employed to facilitate therecovery of RNA or protein expression products at sufficientconcentrations, and to reduce non-specific background. In someinstances, it can be desirable to purify sub-populations of leukocytes,and methods for doing so, such as density or affinity gradients, flowcytometry, fluorescence Activated Cell Sorting (FACS), immuno-magneticseparation, “panning,” and the like, are described in the availableliterature and below.

Obtaining DNA, RNA and Protein Samples for Expression Profiling

Expression patterns can be evaluated at the level of DNA, or RNA orprotein products. For example, a variety of techniques are available forthe isolation of RNA from whole blood. Any technique that allowsisolation of mRNA from cells (in the presence or absence of rRNA andtRNA) can be utilized. In brief, one method that allows reliableisolation of total RNA suitable for subsequent gene expression analysis,is described as follows. Peripheral blood (either venous or arterial) isdrawn from a subject, into one or more sterile, endotoxin free, tubescontaining an anticoagulant (e.g., EDTA, citrate, heparin, etc.).Typically, the sample is divided into at least two portions. Oneportion, e.g., of 5-8 ml of whole blood is frozen and stored for futureanalysis, e.g., of DNA or protein. A second portion, e.g., ofapproximately 8 ml whole blood is processed for isolation of total RNAby any of a variety of techniques as described in, e.g, Sambook,Ausubel, below, as well as U.S. Pat. Nos. 5,728,822 and 4,843,155.

Typically, a subject sample of mononuclear leukocytes obtained fromabout 8 ml of whole blood, a quantity readily available from an adulthuman subject under most circumstances, yields 5-20 μg of total RNA.This amount is ample, e.g., for labeling and hybridization to at leasttwo probe arrays. Labeled probes for analysis of expression patterns ofnucleotides of the candidate libraries are prepared from the subject'ssample of RNA using standard methods. In many cases, cDNA is synthesizedfrom total RNA using a polyT primer and labeled, e.g., radioactive orfluorescent, nucleotides. The resulting labeled cDNA is then hybridizedto probes corresponding to members of the candidate nucleotide library,and expression data is obtained for each nucleotide sequence in thelibrary. RNA isolated from subject samples (e.g., peripheral bloodleukocytes, or leukocytes obtained from other biological fluids andsamples) is next used for analysis of expression patterns of nucleotidesof the candidate libraries.

In some cases, however, the amount of RNA that is extracted from theleukocyte sample is limiting, and amplification of the RNA is desirable.Amplification may be accomplished by increasing the efficiency of probelabeling, or by amplifying the RNA sample prior to labeling. It isappreciated that care must be taken to select an amplification procedurethat does not introduce any bias (with respect to gene expressionlevels) during the amplification process.

Several methods are available that increase the signal from limitingamounts of RNA, e.g. use of the Clontech (Glass Fluorescent LabelingKit) or Stratagene (Fairplay Microarray Labeling Kit), or the Micromaxkit (New England Nuclear, Inc.). Alternatively, cDNA is synthesized fromRNA using a T7-polyT primer, in the absence of label, and DNA dendrimersfrom Genisphere (3DNA Submicro) are hybridized to the poly T sequence onthe primer, or to a different “capture sequence” which is complementaryto a fluorescently labeled sequence. Each 3DNA molecule has 250fluorescent molecules and therefore can strongly label each cDNA.

Alternatively, the RNA sample is amplified prior to labeling. Forexample, linear amplification may be performed, as described in U.S.Pat. No. 6,132,997. A T7-polyT primer is used to generate the cDNA copyof the RNA. A second DNA strand is then made to complete the substratefor amplification. The T7 promoter incorporated into the primer is usedby a T7 polymerase to produce numerous antisense copies of the originalRNA. Fluorescent dye labeled nucleotides are directly incorporated intothe RNA. Alternatively, amino allyl labeled nucleotides are incorporatedinto the RNA, and then fluorescent dyes are chemically coupled to theamino allyl groups, as described in Hughes. Other exemplary methods foramplification are described below.

It is appreciated that the RNA isolated must contain RNA derived fromleukocytes, but may also contain RNA from other cell types to a variabledegree. Additionally, the isolated RNA may come from subsets ofleukocytes, e.g. monocytes and/or T-lymphocytes, as described above.Such consideration of cell type used for the derivation of RNA depend onthe method of expression profiling used.

DNA samples may be obtained for analysis of the presence of DNAmutations, single nucleotide polymorphisms (SNPs), or otherpolymorphisms. DNA is isolated using standard techniques, e.g. Maniatus,supra. Expression of products of candidate nucleotides may also beassessed using proteomics. Protein(s) are detected in samples of patientserum or from leukocyte cellular protein. Serum is prepared bycentrifugation of whole blood, using standard methods. Proteins presentin the serum may have been produced from any of a variety of leukocytesand non-leukocyte cells, and include secreted proteins from leukocytes.Alternatively, leukocytes or a desired sub-population of leukocytes areprepared as described above. Cellular protein is prepared from leukocytesamples using methods well known in the art, e.g., Trizol (InvitrogenLife Technologies, cat #15596108; Chomczynski, P. and Sacchi, N. (1987)Anal. Biochem. 162, 156; Simms, D., Cizdziel, P. E., and Chomczynski, P.(1993) Focus® 15, 99; Chomczynski, P., Bowers-Finn, R., and Sabatini, L.(1987) J. of NIH Res. 6, 83; Chomczynski, P. (1993) Bio/Techniques 15,532; Bracete, A. M., Fox, D. K., and Simms, D. (1998) Focus 20, 82;Sewall, A. and McRae, S. (1998) Focus 20, 36; Anal Biochem 1984 April;138(1):141-3, A method for the quantitative recovery of protein indilute solution in the presence of detergents and lipids; Wessel D,Flugge U I. (1984) Anal Biochem. 1984 April; 138(1):141-143.

Obtaining Expression Patterns

Expression patterns, or profiles, of a plurality of nucleotidescorresponding to members of the candidate library are then evaluated inone or more samples of leukocytes. Typically, the leukocytes are derivedfrom patient peripheral blood samples, although, as indicated above,many other sample sources are also suitable. These expression patternsconstitute a set of relative or absolute expression values for a somenumber of RNAs or protein products corresponding to the plurality ofnucleotide sequences evaluated, which is referred to herein as thesubject's “expression profile” for those nucleotide sequences. Whileexpression patterns for as few as one independent member of thecandidate library can be obtained, it is generally preferable to obtainexpression patterns corresponding to a larger number of nucleotidesequences, e.g., about 2, about 5, about 10, about 20, about 50, about100, about 200, about 500, or about 1000, or more. The expressionpattern for each differentially expressed component member of thelibrary provides a finite specificity and sensitivity with respect topredictive value, e.g., for diagnosis, prognosis, monitoring, and thelike.

Clinical Studies, Data and Patient Groups

For the purpose of discussion, the term subject, or subject sample ofleukocytes, refers to an individual regardless of health and/or diseasestatus. A subject can be a patient, a study participant, a controlsubject, a screening subject, or any other class of individual from whoma leukocyte sample is obtained and assessed in the context of theinvention. Accordingly, a subject can be diagnosed with a disease, canpresent with one or more symptom of a disease, or a predisposing factor,such as a family (genetic) or medical history (medical) factor, for adisease, or the like. Alternatively, a subject can be healthy withrespect to any of the aforementioned factors or criteria. It will beappreciated that the term “healthy” as used herein, is relative to aspecified disease, or disease factor, or disease criterion, as the term“healthy” cannot be defined to correspond to any absolute evaluation orstatus. Thus, an individual defined as healthy with reference to anyspecified disease or disease criterion, can in fact be diagnosed withany other one or more disease, or exhibit any other one or more diseasecriterion.

Furthermore, while the discussion of the invention focuses, and isexemplified using human sequences and samples, the invention is equallyapplicable, through construction or selection of appropriate candidatelibraries, to non-human animals, such as laboratory animals, e.g., mice,rats, guinea pigs, rabbits; domesticated livestock, e.g., cows, horses,goats, sheep, chicken, etc.; and companion animals, e.g., dogs, cats,etc.

Methods for Obtaining Expression Data

Numerous methods for obtaining expression data are known, and any one ormore of these techniques, singly or in combination, are suitable fordetermining expression profiles in the context of the present invention.For example, expression patterns can be evaluated by northern analysis,PCR, RT-PCR, Taq Man analysis, FRET detection, monitoring one or moremolecular beacon, hybridization to an oligonucleotide array,hybridization to a cDNA array, hybridization to a polynucleotide array,hybridization to a liquid microarray, hybridization to a microelectricarray, molecular beacons, cDNA sequencing, clone hybridization, cDNAfragment fingerprinting, serial analysis of gene expression (SAGE),subtractive hybridization, differential display and/or differentialscreening (see, e.g., Lockhart and Winzeler (2000) Nature 405:827-836,and references cited therein).

For example, specific PCR primers are designed to a member(s) of acandidate nucleotide library. cDNA is prepared from subject sample RNAby reverse transcription from a poly-dT oligonucleotide primer, andsubjected to PCR. Double stranded cDNA may be prepared using primerssuitable for reverse transcription of the PCR product, followed byamplification of the cDNA using in vitro transcription. The product ofin vitro transcription is a sense-RNA corresponding to the originalmember(s) of the candidate library. PCR product may be also be evaluatedin a number of ways known in the art, including real-time assessmentusing detection of labeled primers, e.g. TaqMan or molecular beaconprobes. Technology platforms suitable for analysis of PCR productsinclude the ABI 7700, 5700, or 7000 Sequence Detection Systems (AppliedBiosystems, Foster City, Calif.), the MJ Research Opticon (MJ Research,Waltham, Mass.), the Roche Light Cycler (Roche Diagnositics,Indianapolis, Ind.), the Stratagene MX4000 (Stratagene, La Jolla,Calif.), and the Bio-Rad iCycler (Bio-Rad Laboratories, Hercules,Calif.). Alternatively, molecular beacons are used to detect presence ofa nucleic acid sequence in an unamplified RNA or cDNA sample, orfollowing amplification of the sequence using any method, e.g. IVT (InVitro transcription) or NASBA (nucleic acid sequence basedamplification). Molecular beacons are designed with sequencescomplementary to member(s) of a candidate nucleotide library, and arelinked to fluorescent labels. Each probe has a different fluorescentlabel with non-overlapping emission wavelengths. For example, expressionof ten genes may be assessed using ten different sequence-specificmolecular beacons.

Alternatively, or in addition, molecular beacons are used to assessexpression of multiple nucleotide sequences at once. Molecular beaconswith sequence complimentary to the members of a diagnostic nucleotideset are designed and linked to fluorescent labels. Each fluorescentlabel used must have a non-overlapping emission wavelength. For example,10 nucleotide sequences can be assessed by hybridizing 10 sequencespecific molecular beacons (each labeled with a different fluorescentmolecule) to an amplified or un-amplified RNA or cDNA sample. Such anassay bypasses the need for sample labeling procedures.

Alternatively, or in addition bead arrays can be used to assessexpression of multiple sequences at once. See, e.g, LabMAP 100, LuminexCorp, Austin, Tex.). Alternatively, or in addition electric arrays areused to assess expression of multiple sequences, as exemplified by thee-Sensor technology of Motorola (Chicago, Ill.) or Nanochip technologyof Nanogen (San Diego, Calif.)

Of course, the particular method elected will be dependent on suchfactors as quantity of RNA recovered, practitioner preference, availablereagents and equipment, detectors, and the like. Typically, however, theelected method(s) will be appropriate for processing the number ofsamples and probes of interest. Methods for high-throughput expressionanalysis are discussed below.

Alternatively, expression at the level of protein products of geneexpression is performed. For example, protein expression, in a sample ofleukocytes, can be evaluated by one or more method selected from among:western analysis, two-dimensional gel analysis, chromatographicseparation, mass spectrometric detection, protein-fusion reporterconstructs, colorimetric assays, binding to a protein array andcharacterization of polysomal mRNA. One particularly favorable approachinvolves binding of labeled protein expression products to an array ofantibodies specific for members of the candidate library. Methods forproducing and evaluating antibodies are widespread in the art, see,e.g., Coligan, supra; and Harlow and Lane (1989) Antibodies: ALaboratory Manual, Cold Spring Harbor Press, NY (“Harlow and Lane”).Additional details regarding a variety of immunological and immunoassayprocedures adaptable to the present invention by selection of antibodyreagents specific for the products of candidate nucleotide sequences canbe found in, e.g., Stites and Terr (eds.)(1991) Basic and ClinicalImmunology, 7^(th) ed., and Paul, supra. Another approach uses systemsfor performing desorption spectrometry. Commercially available systems,e.g., from Ciphergen Biosystems, Inc. (Fremont, Calif.) are particularlywell suited to quantitative analysis of protein expression. Indeed,Protein Chip® arrays (see, e.g., the web site ciphergen.com) used indesorption spectrometry approaches provide arrays for detection ofprotein expression. Alternatively, affinity reagents, e.g., antibodies,small molecules, etc.) are developed that recognize epitopes of theprotein product. Affinity assays are used in protein array assays, e.g.to detect the presence or absence of particular proteins. Alternatively,affinity reagents are used to detect expression using the methodsdescribed above. In the case of a protein that is expressed on the cellsurface of leukocytes, labeled affinity reagents are bound topopulations of leukocytes, and leukocytes expressing the protein areidentified and counted using fluorescent activated cell sorting (FACS).

It is appreciated that the methods of expression evaluation discussedherein, although discussed in the context of discovery of diagnosticnucleotide sets, are equally applicable for expression evaluation whenusing diagnostic nucleotide sets for, e.g. diagnosis of diseases, asfurther discussed below.

High Throughput Expression Assays

A number of suitable high throughput formats exist for evaluating geneexpression. Typically, the term high throughput refers to a format thatperforms at least about 100 assays, or at least about 500 assays, or atleast about 1000 assays, or at least about 5000 assays, or at leastabout 10,000 assays, or more per day. When enumerating assays, eitherthe number of samples or the number of candidate nucleotide sequencesevaluated can be considered. For example, a northern analysis of, e.g.,about 100 samples performed in a gridded array, e.g., a dot blot, usinga single probe corresponding to a candidate nucleotide sequence can beconsidered a high throughput assay. More typically, however, such anassay is performed as a series of duplicate blots, each evaluated with adistinct probe corresponding to a different member of the candidatelibrary. Alternatively, methods that simultaneously evaluate expressionof about 100 or more candidate nucleotide sequences in one or moresamples, or in multiple samples, are considered high throughput.

Numerous technological platforms for performing high throughputexpression analysis are known. Generally, such methods involve a logicalor physical array of either the subject samples, or the candidatelibrary, or both. Common array formats include both liquid and solidphase arrays. For example, assays employing liquid phase arrays, e.g.,for hybridization of nucleic acids, binding of antibodies or otherreceptors to ligand, etc., can be performed in multiwell, or microtiter,plates. Microtiter plates with 96, 384 or 1536 wells are widelyavailable, and even higher numbers of wells, e.g, 3456 and 9600 can beused. In general, the choice of microtiter plates is determined by themethods and equipment, e.g., robotic handling and loading systems, usedfor sample preparation and analysis. Exemplary systems include, e.g.,the ORCA™ system from Beckman-Coulter, Inc. (Fullerton, Calif.) and theZymate systems from Zymark Corporation (Hopkinton, Mass.).

Alternatively, a variety of solid phase arrays can favorably be employedin to determine expression patterns in the context of the invention.Exemplary formats include membrane or filter arrays (e.g,nitrocellulose, nylon), pin arrays, and bead arrays (e.g., in a liquid“slurry”). Typically, probes corresponding to nucleic acid or proteinreagents that specifically interact with (e.g., hybridize to or bind to)an expression product corresponding to a member of the candidate libraryare immobilized, for example by direct or indirect cross-linking, to thesolid support. Essentially any solid support capable of withstanding thereagents and conditions necessary for performing the particularexpression assay can be utilized. For example, functionalized glass,silicon, silicon dioxide, modified silicon, any of a variety ofpolymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride,polystyrene, polycarbonate, or combinations thereof can all serve as thesubstrate for a solid phase array.

In a preferred embodiment, the array is a “chip” composed, e.g., of oneof the above specified materials. Polynucleotide probes, e.g., RNA orDNA, such as cDNA, synthetic oligonucleotides, and the like, or bindingproteins such as antibodies, that specifically interact with expressionproducts of individual components of the candidate library are affixedto the chip in a logically ordered manner, i.e., in an array. Inaddition, any molecule with a specific affinity for either the sense oranti-sense sequence of the marker nucleotide sequence (depending on thedesign of the sample labeling), can be fixed to the array surfacewithout loss of specific affinity for the marker and can be obtained andproduced for array production, for example, proteins that specificallyrecognize the specific nucleic acid sequence of the marker, ribozymes,peptide nucleic acids (PNA), or other chemicals or molecules withspecific affinity.

Detailed discussion of methods for linking nucleic acids and proteins toa chip substrate, are found in, e.g., U.S. Pat. No. 5,143,854 “LARGESCALE PHOTOLITHOGRAPHIC SOLID PHASE SYNTHESIS OF POLYPEPTIDES ANDRECEPTOR BINDING SCREENING THEREOF” to Pirrung et al., issued, Sep. 1,1992; U.S. Pat. No. 5,837,832 “ARRAYS OF NUCLEIC ACID PROBES ONBIOLOGICAL CHIPS” to Chee et al., issued Nov. 17, 1998; U.S. Pat. No.6,087,112 “ARRAYS WITH MODIFIED OLIGONUCLEOTIDE AND POLYNUCLEOTIDECOMPOSITIONS” to Dale, issued Jul. 11, 2000; U.S. Pat. No. 5,215,882“METHOD OF IMMOBILIZING NUCLEIC ACID ON A SOLID SUBSTRATE FOR USE INNUCLEIC ACID HYBRIDIZATION ASSAYS” to Bahl et al., issued Jun. 1, 1993;U.S. Pat. No. 5,707,807 “MOLECULAR INDEXING FOR EXPRESSED GENE ANALYSIS”to Kato, issued Jan. 13, 1998; U.S. Pat. No. 5,807,522 “METHODS FORFABRICATING MICROARRAYS OF BIOLOGICAL SAMPLES” to Brown et al., issuedSep. 15, 1998; U.S. Pat. No. 5,958,342 “JET DROPLET DEVICE” to Gamble etal., issued Sep. 28, 1999; U.S. Pat. No. 5,994,076 “METHODS OF ASSAYINGDIFFERENTIAL EXPRESSION” to Chenchik et al., issued Nov. 30, 1999; U.S.Pat. No. 6,004,755 “QUANTITATIVE MICROARRAY HYBRIDIZATION ASSAYS” toWang, issued Dec. 21, 1999; U.S. Pat. No. 6,048,695 “CHEMICALLY MODIFIEDNUCLEIC ACIDS AND METHOD FOR COUPLING NUCLEIC ACIDS TO SOLID SUPPORT” toBradley et al., issued Apr. 11, 2000; U.S. Pat. No. 6,060,240 “METHODSFOR MEASURING RELATIVE AMOUNTS OF NUCLEIC ACIDS IN A COMPLEX MIXTURE ANDRETRIEVAL OF SPECIFIC SEQUENCES THEREFROM” to Kamb et al., issued May 9,2000; U.S. Pat. No. 6,090,556 “METHOD FOR QUANTITATIVELY DETERMINING THEEXPRESSION OF A GENE” to Kato, issued Jul. 18, 2000; and U.S. Pat. No.6,040,138 “EXPRESSION MONITORING BY HYBRIDIZATION TO HIGH DENSITYOLIGONUCLEOTIDE ARRAYS” to Lockhart et al., issued Mar. 21, 2000 each ofwhich are hereby incorporated by reference in their entirety.

For example, cDNA inserts corresponding to candidate nucleotidesequences, in a standard TA cloning vector are amplified by a polymerasechain reaction for approximately 30-40 cycles. The amplified PCRproducts are then arrayed onto a glass support by any of a variety ofwell known techniques, e.g., the VSLIPS™ technology described in U.S.Pat. No. 5,143,854. RNA, or cDNA corresponding to RNA, isolated from asubject sample of leukocytes is labeled, e.g., with a fluorescent tag,and a solution containing the RNA (or cDNA) is incubated underconditions favorable for hybridization, with the “probe” chip. Followingincubation, and washing to eliminate non-specific hybridization, thelabeled nucleic acid bound to the chip is detected qualitatively orquantitatively, and the resulting expression profile for thecorresponding candidate nucleotide sequences is recorded. It isappreciated that the probe used for diagnostic purposes may be identicalto the probe used during diagnostic nucleotide sequence discovery andvalidation. Alternatively, the probe sequence may be different than thesequence used in diagnostic nucleotide sequence discovery andvalidation. Multiple cDNAs from a nucleotide sequence that arenon-overlapping or partially overlapping may also be used.

In another approach, oligonucleotides corresponding to members of acandidate nucleotide library are synthesized and spotted onto an array.Alternatively, oligonucleotides are synthesized onto the array usingmethods known in the art, e.g. Hughes, et al. supra. The oligonucleotideis designed to be complementary to any portion of the candidatenucleotide sequence. In addition, in the context of expression analysisfor, e.g. diagnostic use of diagnostic nucleotide sets, anoligonucleotide can be designed to exhibit particular hybridizationcharacteristics, or to exhibit a particular specificity and/orsensitivity, as further described below.

Hybridization signal may be amplified using methods known in the art,and as described herein, for example use of the Clontech kit (GlassFluorescent Labeling Kit), Stratagene kit (Fairplay Microarray LabelingKit), the Micromax kit (New England Nuclear, Inc.), the Genisphere kit(3DNA Submicro), linear amplification, e.g. as described in U.S. Pat.No. 6,132,997 or described in Hughes, T R, et al., Nature Biotechnology,19:343-347 (2001) and/or Westin et al. Nat. Biotech. 18:199-204.

Alternatively, fluorescently labeled cDNA are hybridized directly to themicroarray using methods known in the art. For example, labeled cDNA aregenerated by reverse transcription using Cy3- and Cy5-conjugateddeoxynucleotides, and the reaction products purified using standardmethods. It is appreciated that the methods for signal amplification ofexpression data useful for identifying diagnostic nucleotide sets arealso useful for amplification of expression data for diagnosticpurposes.

Microarray expression may be detected by scanning the microarray with avariety of laser or CCD-based scanners, and extracting features withnumerous software packages, for example, Imagene (Biodiscovery), FeatureExtraction (Agilent), Scanalyze (Eisen, M. 1999. SCANALYZE User Manual;Stanford Univ., Stanford, Calif. Ver 2.32.), GenePix (Axon Instruments).

In another approach, hybridization to microelectric arrays is performed,e.g. as described in Umek et al (2001) J Mol. Diagn. 3:74-84. Anaffinity probe, e.g. DNA, is deposited on a metal surface. The metalsurface underlying each probe is connected to a metal wire andelectrical signal detection system. Unlabelled RNA or cDNA is hybridizedto the array, or alternatively, RNA or cDNA sample is amplified beforehybridization, e.g. by PCR. Specific hybridization of sample RNA or cDNAresults in generation of an electrical signal, which is transmitted to adetector. See Westin (2000) Nat. Biotech. 18:199-204 (describinganchored multiplex amplification of a microelectronic chip array); Edman(1997) NAR 25:4907-14; Vignali (2000) J Immunol Methods 243:243-55.

In another approach, a microfluidics chip is used for RNA samplepreparation and analysis. This approach increases efficiency becausesample preparation and analysis are streamlined. Briefly, microfluidicsmay be used to sort specific leukocyte sub-populations prior to RNApreparation and analysis. Microfluidics chips are also useful for, e.g.,RNA preparation, and reactions involving RNA (reverse transcription,RT-PCR). Briefly, a small volume of whole, anti-coagulated blood isloaded onto a microfluidics chip, for example chips available fromCaliper (Mountain View, Calif.) or Nanogen (San Diego, Calif.) Amicrofluidics chip may contain channels and reservoirs in which cellsare moved and reactions are performed. Mechanical, electrical, magnetic,gravitational, centrifugal or other forces are used to move the cellsand to expose them to reagents. For example, cells of whole blood aremoved into a chamber containing hypotonic saline, which results inselective lysis of red blood cells after a 20-minute incubation. Next,the remaining cells (leukocytes) are moved into a wash chamber andfinally, moved into a chamber containing a lysis buffer such asguanidine isothyocyanate. The leukocyte cell lysate is further processedfor RNA isolation in the chip, or is then removed for furtherprocessing, for example, RNA extraction by standard methods.Alternatively, the microfluidics chip is a circular disk containingficoll or another density reagent. The blood sample is injected into thecenter of the disc, the disc is rotated at a speed that generates acentrifugal force appropriate for density gradient separation ofmononuclear cells, and the separated mononuclear cells are thenharvested for further analysis or processing.

It is understood that the methods of expression evaluation, above,although discussed in the context of discovery of diagnostic nucleotidesets, are also applicable for expression evaluation when usingdiagnostic nucleotide sets for, e.g. diagnosis of diseases, as furtherdiscussed below.

Evaluation of Expression Patterns

Expression patterns can be evaluated by qualitative and/or quantitativemeasures. Certain of the above described techniques for evaluating geneexpression (as RNA or protein products) yield data that arepredominantly qualitative in nature. That is, the methods detectdifferences in expression that classify expression into distinct modeswithout providing significant information regarding quantitative aspectsof expression. For example, a technique can be described as aqualitative technique if it detects the presence or absence ofexpression of a candidate nucleotide sequence, i.e., an on/off patternof expression. Alternatively, a qualitative technique measures thepresence (and/or absence) of different alleles, or variants, of a geneproduct.

In contrast, some methods provide data that characterizes expression ina quantitative manner. That is, the methods relate expression on anumerical scale, e.g., a scale of 0-5, a scale of 1-10, a scale of+-+++, from grade 1 to grade 5, a grade from a to z, or the like. Itwill be understood that the numerical, and symbolic examples providedare arbitrary, and that any graduated scale (or any symbolicrepresentation of a graduated scale) can be employed in the context ofthe present invention to describe quantitative differences in nucleotidesequence expression. Typically, such methods yield informationcorresponding to a relative increase or decrease in expression.

Any method that yields either quantitative or qualitative expressiondata is suitable for evaluating expression of candidate nucleotidesequence in a subject sample of leukocytes. In some cases, e.g., whenmultiple methods are employed to determine expression patterns for aplurality of candidate nucleotide sequences, the recovered data, e.g.,the expression profile, for the nucleotide sequences is a combination ofquantitative and qualitative data.

In some applications, expression of the plurality of candidatenucleotide sequences is evaluated sequentially. This is typically thecase for methods that can be characterized as low- tomoderate-throughput. In contrast, as the throughput of the elected assayincreases, expression for the plurality of candidate nucleotidesequences in a sample or multiple samples of leukocytes, is assayedsimultaneously. Again, the methods (and throughput) are largelydetermined by the individual practitioner, although, typically, it ispreferable to employ methods that permit rapid, e.g. automated orpartially automated, preparation and detection, on a scale that istime-efficient and cost-effective.

It is understood that the preceding discussion, while directed at theassessment of expression of the members of candidate libraries, is alsoapplies to the assessment of the expression of members of diagnosticnucleotide sets, as further discussed below.

Genotyping

In addition to, or in conjunction with the correlation of expressionprofiles and clinical data, it is often desirable to correlateexpression patterns with the subject's genotype at one or more geneticloci. The selected loci can be, for example, chromosomal locicorresponding to one or more member of the candidate library,polymorphic alleles for marker loci, or alternative disease related loci(not contributing to the candidate library) known to be, or putativelyassociated with, a disease (or disease criterion). Indeed, it will beappreciated, that where a (polymorphic) allele at a locus is linked to adisease (or to a predisposition to a disease), the presence of theallele can itself be a disease criterion.

Numerous well known methods exist for evaluating the genotype of anindividual, including southern analysis, restriction fragment lengthpolymorphism (RFLP) analysis, polymerase chain reaction (PCR),amplification length polymorphism (AFLP) analysis, single strandedconformation polymorphism (SSCP) analysis, single nucleotidepolymorphism (SNP) analysis (e.g., via PCR, Taqman or molecularbeacons), among many other useful methods. Many such procedures arereadily adaptable to high throughput and/or automated (orsemi-automated) sample preparation and analysis methods. Most, can beperformed on nucleic acid samples recovered via simple procedures fromthe same sample of leukocytes as yielded the material for expressionprofiling. Exemplary techniques are described in, e.g., Sambrook, andAusubel, supra.

Identification of the Diagnostic Nucleotide Sets of the Invention

Identification of diagnostic nucleotide sets and disease specific targetnucleotide sequence proceeds by correlating the leukocyte expressionprofiles with data regarding the subject's health status to produce adata set designated a “molecular signature.” Examples of data regardinga patient's health status, also termed “disease criteria(ion)”, isdescribed below and in the Section titled “selected diseases,” below.Methods useful for correlation analysis are further described elsewherein the specification.

Generally, relevant data regarding the subject's health status includesretrospective or prospective health data, e.g., in the form of thesubject's medical history, as provided by the subject, physician orthird party, such as, medical diagnoses, laboratory test results,diagnostic test results, clinical events, or medication lists, asfurther described below. Such data may include information regarding apatient's response to treatment and/or a particular medication and dataregarding the presence of previously characterized “risk factors.” Forexample, cigarette smoking and obesity are previously identified riskfactors for heart disease. Further examples of health statusinformation, including diseases and disease criteria, is described inthe section titled Selected diseases, below.

Typically, the data describes prior events and evaluations (i.e.,retrospective data). However, it is envisioned that data collectedsubsequent to the sampling (i.e., prospective data) can also becorrelated with the expression profile. The tissue sampled, e.g.,peripheral blood, bronchial lavage, etc., can be obtained at one or moremultiple time points and subject data is considered retrospective orprospective with respect to the time of sample procurement.

Data collected at multiple time points, called “longitudinal data”, isoften useful, and thus, the invention encompasses the analysis ofpatient data collected from the same patient at different time points.Analysis of paired samples, such as samples from a patient at differenttime, allows identification of differences that are specifically relatedto the disease state since the genetic variability specific to thepatient is controlled for by the comparison. Additionally, othervariables that exist between patients may be controlled for in this way,for example, the presence or absence of inflammatory diseases (e.g.,rheumatoid arthritis) the use of medications that may effect leukocytegene expression, the presence or absence of co-morbid conditions, etc.Methods for analysis of paired samples are further described below.Moreover, the analysis of a pattern of expression profiles (generated bycollecting multiple expression profiles) provides information relatingto changes in expression level over time, and may permit thedetermination of a rate of change, a trajectory, or an expression curve.Two longitudinal samples may provide information on the change inexpression of a gene over time, while three longitudinal samples may benecessary to determine the “trajectory” of expression of a gene. Suchinformation may be relevant to the diagnosis of a disease. For example,the expression of a gene may vary from individual to individual, but aclinical event, for example, a heart attack, may cause the level ofexpression to double in each patient. In this example, clinicallyinteresting information is gleaned from the change in expression level,as opposed to the absolute level of expression in each individual.

When a single patient sample is obtained, it may still be desirable tocompare the expression profile of that sample to some referenceexpression profile. In this case, one can determine the change ofexpression between the patient's sample and a reference expressionprofile that is appropriate for that patient and the medical conditionin question. For example, a reference expression profile can bedetermined for all patients without the disease criterion in questionwho have similar characteristics, such as age, sex, race, diagnoses etc.

Generally, small sample sizes of 10-40 samples from 10-20 individualsare used to identify a diagnostic nucleotide set. Larger sample sizesare generally necessary to validate the diagnostic nucleotide set foruse in large and varied patient populations, as further described below.For example, extension of gene expression correlations to varied ethnicgroups, demographic groups, nations, peoples or races may requireexpression correlation experiments on the population of interest.

Expression Reference Standards

Expression profiles derived from a patient (i.e., subjects diagnosedwith, or exhibiting symptoms of, or exhibiting a disease criterion, orunder a doctor's care for a disease) sample are compared to a control orstandard expression RNA to facilitate comparison of expression profiles(e.g. of a set of candidate nucleotide sequences) from a group ofpatients relative to each other (i.e., from one patient in the group toother patients in the group, or to patients in another group).

The reference RNA used should have desirable features of low cost andsimplicity of production on a large scale. Additionally, the referenceRNA should contain measurable amounts of as many of the genes of thecandidate library as possible.

For example, in one approach to identifying diagnostic nucleotide sets,expression profiles derived from patient samples are compared to aexpression reference “standard.” Standard expression reference can be,for example, RNA derived from resting cultured leukocytes orcommercially available reference RNA, such as Universal reference RNAfrom Stratagene. See Nature, V406, 8-17-00, p. 747-752. Use of anexpression reference standard is particularly useful when the expressionof large numbers of nucleotide sequences is assayed, e.g. in an array,and in certain other applications, e.g. qualitative PCR, RT-PCR, etc.,where it is desirable to compare a sample profile to a standard profile,and/or when large numbers of expression profiles, e.g. a patientpopulation, are to be compared. Generally, an expression referencestandard should be available in large quantities, should be a goodsubstrate for amplification and labeling reactions, and should becapable of detecting a large percentage of candidate nucleic acids usingsuitable expression profiling technology.

Alternatively, or in addition, the expression profile derived from apatient sample is compared with the expression of an internal referencecontrol gene, for example, β-actinor CD4. The relative expression of theprofiled genes and the internal reference control gene (from the sameindividual) is obtained. An internal reference control may also be usedwith a reference RNA. For example, an expression profile for “gene 1”and the gene encoding CD4 can be determined in a patient sample and in areference RNA. The expression of each gene can be expressed as the“relative” ratio of expression the gene in the patient sample comparedwith expression of the gene in the reference RNA. The expression ratio(sample/reference) for gene 1 may be divided by the expression rationfor CD4 (sample/reference) and thus the relative expression of gene 1 toCD4 is obtained.

The invention also provides a buffy coat control RNA useful forexpression profiling, and a method of using control RNA produced from apopulation of buffy coat cells, the white blood cell layer derived fromthe centrifugation of whole blood. Buffy coat contains all white bloodcells, including granulocytes, mononuclear cells and platelets. Theinvention also provides a method of preparing control RNA from buffycoat cells for use in expression profile analysis of leukocytes. Buffycoat fractions are obtained, e.g. from a blood bank or directly fromindividuals, preferably from a large number of individuals such thatbias from individual samples is avoided and so that the RNA samplerepresents an average expression of a healthy population. Buffy coatfractions from about 50 or about 100, or more individuals are preferred.10 ml buffy coat from each individual is used. Buffy coat samples aretreated with an erthythrocyte lysis buffer, so that erthythrocytes areselectively removed. The leukocytes of the buffy coat layer arecollected by centrifugation. Alternatively, the buffy cell sample can befurther enriched for a particular leukocyte sub-populations, e.g.mononuclear cells, T-lymphocytes, etc. To enrich for mononuclear cells,the buffy cell pellet, above, is diluted in PBS (phosphate bufferedsaline) and loaded onto a non-polystyrene tube containing a polysucroseand sodium diatrizoate solution adjusted to a density of 1.077+/−0.001g/ml. To enrich for T-lymphocytes, 45 ml of whole blood is treated withRosetteSep (Stem Cell Technologies), and incubated at room temperaturefor 20 minutes. The mixture is diluted with an equal volume of PBS plus2% FBS and mixed by inversion. 30 ml of diluted mixture is layered ontop of 15 ml DML medium (Stem Cell Technologies). The tube iscentrifuged at 1200×g, and the enriched cell layer at the plasma: mediuminterface is removed, washed with PBS+2% FBS, and cells collected bycentrifugation at 1200×g. The cell pellet is treated with 5 ml oferythrocyte lysis buffer (EL buffer, Qiagen) for 10 minutes on ice, andenriched T-lymphoctes are collected by centrifugation.

In addition or alternatively, the buffy cells (whole buffy coat orsub-population, e.g. mononuclear fraction) can be cultured in vitro andsubjected to stimulation with cytokines or activating chemicals such asphorbol esters or ionomycin. Such stimuli may increase expression ofnucleotide sequences that are expressed in activated immune cells andmight be of interest for leukocyte expression profiling experiments.

Following sub-population selection and/or further treatment, e.g.stimulation as described above, RNA is prepared using standard methods.For example, cells are pelleted and lysed with a phenol/guanidiniumthiocyanate and RNA is prepared. RNA can also be isolated using a silicagel-based purification column or the column method can be used on RNAisolated by the phenol/guanidinium thiocyanate method. RNA fromindividual buffy coat samples can be pooled during this process, so thatthe resulting reference RNA represents the RNA of many individuals andindividual bias is minimized or eliminated. In addition, a new batch ofbuffy coat reference RNA can be directly compared to the last batch toensure similar expression pattern from one batch to another, usingmethods of collecting and comparing expression profiles describedabove/below. One or more expression reference controls are used in anexperiment. For example, RNA derived from one or more of the followingsources can be used as controls for an experiment: stimulated orunstimulated whole buffy coat, stimulated or unstimulated peripheralmononuclear cells, or stimulated or unstimulated T-lymphocytes.

Alternatively, the expression reference standard can be derived from anysubject or class of subjects including healthy subjects or subjectsdiagnosed with the same or a different disease or disease criterion.Expression profiles from subjects in two distinct classes are comparedto determine which subset of nucleotide sequences in the candidatelibrary best distinguish between the two subject classes, as furtherdiscussed below. It will be appreciated that in the present context, theterm “distinct classes” is relevant to at least one distinguishablecriterion relevant to a disease of interest, a “disease criterion.” Theclasses can, of course, demonstrate significant overlap (or identity)with respect to other disease criteria, or with respect to diseasediagnoses, prognoses, or the like. The mode of discovery involves, e.g.,comparing the molecular signature of different subject classes to eachother (such as patient to control, patients with a first diagnosis topatients with a second diagnosis, etc.) or by comparing the molecularsignatures of a single individual taken at different time points. Theinvention can be applied to a broad range of diseases, disease criteria,conditions and other clinical and/or epidemiological questions, asfurther discussed above/below.

It is appreciated that while the present discussion pertains to the useof expression reference controls while identifying diagnostic nucleotidesets, expression reference controls are also useful during use ofdiagnostic nucleotide sets, e.g. use of a diagnostic nucleotide set fordiagnosis of a disease, as further described below.

Analysis of Expression Profiles

In order to facilitate ready access, e.g., for comparison, review,recovery, and/or modification, the molecular signatures/expressionprofiles are typically recorded in a database. Most typically, thedatabase is a relational database accessible by a computational device,although other formats, e.g., manually accessible indexed files ofexpression profiles as photographs, analogue or digital imagingreadouts, spreadsheets, etc. can be used. Further details regardingpreferred embodiments are provided below. Regardless of whether theexpression patterns initially recorded are analog or digital in natureand/or whether they represent quantitative or qualitative differences inexpression, the expression patterns, expression profiles (collectiveexpression patterns), and molecular signatures (correlated expressionpatterns) are stored digitally and accessed via a database. Typically,the database is compiled and maintained at a central facility, withaccess being available locally and/or remotely.

As additional samples are obtained, and their expression profilesdetermined and correlated with relevant subject data, the ensuingmolecular signatures are likewise recorded in the database. However,rather than each subsequent addition being added in an essentiallypassive manner in which the data from one sample has little relation todata from a second (prior or subsequent) sample, the algorithmsoptionally additionally query additional samples against the existingdatabase to further refine the association between a molecular signatureand disease criterion. Furthermore, the data set comprising the one (ormore) molecular signatures is optionally queried against an expandingset of additional or other disease criteria. The use of the database inintegrated systems and web embodiments is further described below.

Analysis of Expression Profile Data from Arrays

Expression data is analyzed using methods well known in the art,including the software packages Imagene (Biodiscovery, Marina del Rey,Calif.), Feature Extraction Software (Agilent, Palo Alto, Calif.), andScanalyze (Stanford University). In the discussion that follows, a“feature” refers to an individual spot of DNA on an array. Each gene maybe represented by more than one feature. For example, hybridizedmicroarrays are scanned and analyzed on an Axon Instruments scannerusing GenePix 3.0 software (Axon Instruments, Union City, Calif.). Thedata extracted by GenePix is used for all downstream quality control andexpression evaluation. The data is derived as follows. The data for allfeatures flagged as “not found” by the software is removed from thedataset for individual hybridizations. The “not found” flag by GenePixindicates that the software was unable to discriminate the feature fromthe background. Each feature is examined to determine the value of itssignal. The median pixel intensity of the background (B_(n)) issubtracted from the median pixel intensity of the feature (F_(n)) toproduce the background-subtracted signal (hereinafter, “BGSS”). The BGSSis divided by the standard deviation of the background pixels to providethe signal-to-noise ratio (hereinafter, “S/N”). Features with a S/N ofthree or greater in both the Cy3 channel (corresponding to the sampleRNA) and Cy5 channel (corresponding to the reference RNA) are used forfurther analysis (hereinafter denoted “useable features”).Alternatively, different S/Ns are used for selecting expression data foran analysis. For example, only expression data with signal to noiseratios >3 might be used in an analysis. Alternatively, features with S/Nvalues <3 may be flagged as such and included in the analysis. Suchflagged data sets include more values and may allow one to discoverexpression markers that would be missed otherwise. However, such datasets may have a higher variablilty than filtered data, which maydecrease significance of findings or performance of correlationstatistics.

For each usable feature (i), the expression level (e) is expressed asthe logarithm of the ratio (R) of the Background Subtracted Signal(hereinafter “BGSS”) for the Cy3 (sample RNA) channel divided by theBGSS for the Cy5 channel (reference RNA). This “log ratio” value is usedfor comparison to other experiments.

$\begin{matrix}{R_{i} = \frac{{BGSS}_{sample}}{{BGSS}_{reference}}} & (0.1) \\{e_{i} = {\log \; r_{i}}} & (0.2)\end{matrix}$

Variation in signal across hybridizations may be caused by a number offactors affecting hybridization, DNA spotting, wash conditions, andlabeling efficiency.

A single reference RNA may be used with all of the experimental RNAs,permitting multiple comparisons in addition to individual comparisons.By comparing sample RNAs to the same reference, the gene expressionlevels from each sample are compared across arrays, permitting the useof a consistent denominator for our experimental ratios.

Scaling

The data may be scaled (normalized) to control for labeling andhybridization variability within the experiment, using methods known inthe art. Scaling is desirable because it facilitates the comparison ofdata between different experiments, patients, etc. Generally the BGSSare scaled to a factor such as the median, the mean, the trimmed mean,and percentile. Additional methods of scaling include: to scale between0 and 1, to subtract the mean, or to subtract the median.

Scaling is also performed by comparison to expression patterns obtainedusing a common reference RNA, as described in greater detail above. Aswith other scaling methods, the reference RNA facilitates multiplecomparisons of the expression data, e.g., between patients, betweensamples, etc. Use of a reference RNA provides a consistent denominatorfor experimental ratios.

In addition to the use of a reference RNA, individual expression levelsmay be adjusted to correct for differences in labeling efficiencybetween different hybridization experiments, allowing direct comparisonbetween experiments with different overall signal intensities, forexample. A scaling factor (a) may be used to adjust individualexpression levels as follows. The median of the scaling factor (a), forexample, BGSS, is determined for the set of all features with a S/Ngreater than three. Next, the BGSS, (the BGSS for each feature “i”) isdivided by the median for all features (a), generating a scaled ratio.The scaled ration is used to determine the expression value for thefeature (e_(i)), or the log ratio.

$\begin{matrix}{S_{i} = \frac{{BGSS}_{i}}{a}} & (0.3) \\{e_{i} = {\log \left( \frac{{Cy}\; 3\; S_{i}}{{Cy}\; 5\; S_{i}} \right)}} & (0.4)\end{matrix}$

In addition, or alternatively, control features are used to normalizethe data for labeling and hybridization variability within theexperiment. Control feature may be cDNA for genes from the plant,Arabidopsis thaliana, that are included when spotting the mini-array.Equal amounts of RNA complementary to control cDNAs are added to each ofthe samples before they were labeled. Using the signal from thesecontrol genes, a normalization constant (L) is determined according tothe following formula:

$L_{j} = \frac{\frac{\sum\limits_{i = 1}^{N}{BGSS}_{j,i}}{N}}{\frac{\sum\limits_{j = 1}^{K}\frac{\sum\limits_{i = 1}^{N}{BGSS}_{j,i}}{N}}{K}}$

where BGSS, is the signal for a specific feature, N is the number of A.thaliana control features, K is the number of hybridizations, and L_(j)is the normalization constant for each individual hybridization.

Using the formula above, the mean for all control features of aparticular hybridization and dye (e.g., Cy3) is calculated. The controlfeature means for all Cy3 hybridizations are averaged, and the controlfeature mean in one hybridization divided by the average of allhybridizations to generate a normalization constant for that particularCy3 hybridization (L_(j)), which is used as a in equation (0.3). Thesame normalization steps may be performed for Cy3 and Cy5 values.

Many additional methods for normalization exist and can be applied tothe data. In one method, the average ratio of Cy3 BGSS/Cy5 BGSS isdetermined for all features on an array. This ratio is then scaled tosome arbitrary number, such as 1 or some other number. The ratio foreach probe is then multiplied by the scaling factor required to bringthe average ratio to the chosen level. This is performed for each arrayin an analysis. Alternatively, the ratios are normalized to the averageratio across all arrays in an analysis.

If multiple features are used per gene sequence or oligonucleotide,these repeats can be used to derive an average expression value for eachgene. If some of the replicate features are of poor quality and don'tmeet requirements for analysis, the remaining features can be used torepresent the gene or gene sequence.

Correlation Analysis

Correlation analysis is performed to determine which array probes haveexpression behavior that best distinguishes or serves as markers forrelevant groups of samples representing a particular clinical condition.Correlation analysis, or comparison among samples representing differentdisease criteria (e.g., clinical conditions), is performed usingstandard statistical methods. Numerous algorithms are useful forcorrelation analysis of expression data, and the selection of algorithmsdepends in part on the data analysis to be performed. For example,algorithms can be used to identify the single most informative gene withexpression behavior that reliably classifies samples, or to identify allthe genes useful to classify samples. Alternatively, algorithms can beapplied that determine which set of 2 or more genes have collectiveexpression behavior that accurately classifies samples. The use ofmultiple expression markers for diagnostics may overcome the variabilityin expression of a gene between individuals, or overcome the variabilityintrinsic to the assay. Multiple expression markers may includeredundant markers (surrogates), in that two or more genes or probes mayprovide the same information with respect to diagnosis. This may occur,for example, when two or more genes or gene probes are coordinatelyexpressed. For diagnostic application, it may be appropriate to utilizea gene and one or more of its surrogates in the assay. This redundancymay overcome failures (technical or biological) of a single marker todistinguish samples. Alternatively, one or more surrogates may haveproperties that make them more suitable for assay development, such as ahigher baseline level of expression, better cell specificity, a higherfold change between sample groups or more specific sequence for thedesign of PCR primers or complimentary probes. It will be appreciatedthat while the discussion above pertains to the analysis of RNAexpression profiles the discussion is equally applicable to the analysisof profiles of proteins or other molecular markers.

Prior to analysis, expression profile data may be formatted or preparedfor analysis using methods known in the art. For example, often the logratio of scaled expression data for every array probe is calculatedusing the following formula:

log(Cy 3 BGSS/Cy5 BGSS), where Cy 3 signal corresponds to the expressionof the gene in the clinical sample, and Cy5 signal corresponds toexpression of the gene in the reference RNA.

Data may be further filtered depending on the specific analysis to bedone as noted below. For example, filtering may be aimed at selectingonly samples with expression above a certain level, or probes withvariability above a certain level between sample sets.

The following non-limiting discussion consider several statisticalmethods known in the art. Briefly, the t-test and ANOVA are used toidentify single genes with expression differences between or amongpopulations, respectively. Multivariate methods are used to identify aset of two or more genes for which expression discriminates between twodisease states more specifically than expression of any single gene.

t-Test

The simplest measure of a difference between two groups is the Student'st test. See, e.g., Welsh et al. (2001) Proc Natl Acad Sci USA 98:1176-81(demonstrating the use of an unpaired Student's t-test for the discoveryof differential gene expression in ovarian cancer samples and controltissue samples). The t-test assumes equal variance and normallydistributed data. This test identifies the probability that there is adifference in expression of a single gene between two groups of samples.The number of samples within each group that is required to achievestatistical significance is dependent upon the variation among thesamples within each group. The standard formula for a t-test is:

$\begin{matrix}{{{t\left( e_{i} \right)} = \frac{{\overset{\_}{e}}_{i,c} - {\overset{\_}{e}}_{i,t}}{\sqrt{\left( {s_{i,c}^{2}/n_{c}} \right) + \left( {s_{i,t}^{2}/n_{t}} \right)}}},} & (0.5)\end{matrix}$

where ē_(i) is the difference between the mean expression level of genei in groups c and t, s_(i,c) is the variance of gene x in group c ands_(i,t) is the variance of gene x in group t. n_(c) and n_(t) are thenumbers of samples in groups c and t.

The combination of the t statistic and the degrees of freedom[min(n_(t), n_(c))−1] provides a p value, the probability of rejectingthe null hypothesis. A p-value of ≦0.01, signifying a 99 percentprobability the mean expression levels are different between the twogroups (a 1% chance that the mean expression levels are in fact notdifferent and that the observed difference occurred by statisticalchance), is often considered acceptable.

When performing tests on a large scale, for example, on a large datasetof about 8000 genes, a correction factor must be included to adjust forthe number of individual tests being performed. The most common andsimplest correction is the Bonferroni correction for multiple tests,which divides the p-value by the number of tests run. Using this test onan 8000 member dataset indicates that a p value of ≦0.00000125 isrequired to identify genes that are likely to be truly different betweenthe two test conditions.

Significance Analysis for Microarrays (SAM)

Significance analysis for microarrays (SAM) (Tusher 2001) is a methodthrough which genes with a correlation between their expression valuesand the response vector are statistically discovered and assigned astatistical significance. The ratio of false significant to significantgenes is the False Discovery Rate (FDR). This means that for eachthreshold there are a set of genes which are called significant, and theFDR gives a confidence level for this claim. If a gene is calleddifferentially expressed between 2 classes by SAM, with a FDR of 5%,there is a 95% chance that the gene is actually differentially expressedbetween the classes. SAM takes intoaccount the variability and largenumber of variables of microarrays. SAM will identity genes that aremost globally differentially expressed between the classes. Thus,important genes for identifying and classifying outlier samples orpatients may not be identified by SAM.

Wilcoxon's Signed Ranks Test

This method is non-parametric and is utilized for paired comparisons.See e.g., Sokal and Rohlf (1987) Introduction to Biostatistics 2^(nd)edition, WH Freeman, New York. At least 6 pairs are necessary to applythis statistic. This test is useful for analysis of paired expressiondata (for example, a set of patients who have cardiac transplant biopsyon 2 occasions and have a grade 0 on one occasion and a grade 3A onanother).

ANOVA

Differences in gene expression across multiple related groups may beassessed using an Analysis of Variance (ANOVA), a method well known inthe art (Michelson and Schofield, 1996).

Multivariate Analysis

Many algorithms suitable for multivariate analysis are known in the art.Generally, a set of two or more genes for which expression discriminatesbetween two disease states more specifically than expression of anysingle gene is identified by searching through the possible combinationsof genes using a criterion for discrimination, for example theexpression of gene X must increase from normal 300 percent, while theexpression of genes Y and Z must decrease from normal by 75 percent.Ordinarily, the search starts with a single gene, then adds the nextbest fit at each step of the search. Alternatively, the search startswith all of the genes and genes that do not aid in the discriminationare eliminated step-wise.

Paired Samples

Paired samples, or samples collected at different time-points from thesame patient, are often useful, as described above. For example, use ofpaired samples permits the reduction of variation due to geneticvariation among individuals. In addition, the use of paired samples hasa statistical significance, in that data derived from paired samples canbe calculated in a different manner that recognizes the reducedvariability. For example, the formula for a t-test for paired samplesis:

$\begin{matrix}{{{t\left( e_{x} \right)} = \frac{{\overset{\_}{D}}_{\overset{\_}{e}x}}{\sqrt{\frac{{\sum D^{2}} - {\left( {\sum D} \right)^{2}/b}}{b - 1}}}},} & (0.5)\end{matrix}$

where D is the difference between each set of paired samples and b isthe number of sample pairs. D is the mean of the differences between themembers of the pairs. In this test, only the differences between thepaired samples are considered, then grouped together (as opposed totaking all possible differences between groups, as would be the casewith an ordinary t-test). Additional statistical tests useful withpaired data, e.g., ANOVA and Wilcoxon's signed rank test, are discussedabove.

Diagnostic Classification

Once a discriminating set of genes is identified, the diagnosticclassifier (a mathematical function that assigns samples to diagnosticcategories based on expression data) is applied to unknown sampleexpression levels.

Methods that can be used for this analysis include the followingnon-limiting list:

CLEAVER is an algorithm used for classification of useful expressionprofile data. See Raychaudhuri et al. (2001) Trends Biotechnol19:189-193. CLEAVER uses positive training samples (e.g., expressionprofiles from samples known to be derived from a particular patient orsample diagnostic category, disease or disease criteria), negativetraining samples (e.g., expression profiles from samples known not to bederived from a particular patient or sample diagnostic category, diseaseor disease criteria) and test samples (e.g., expression profilesobtained from a patient), and determines whether the test samplecorrelates with the particular disease or disease criteria, or does notcorrelate with a particular disease or disease criteria. CLEAVER alsogenerates a list of the 20 most predictive genes for classification.

Artificial neural networks (hereinafter, “ANN”) can be used to recognizepatterns in complex data sets and can discover expression criteria thatclassify samples into more than 2 groups. The use of artificial neuralnetworks for discovery of gene expression diagnostics for cancers usingexpression data generated by oligonucleotide expression microarrays isdemonstrated by Khan et al. (2001) Nature Med. 7:673-9. Khan found that96 genes provided 0% error rate in classification of the tumors. Themost important of these genes for classification was then determined bymeasuring the sensitivity of the classification to a change inexpression of each gene. Hierarchical clustering using the 96 genesresults in correct grouping of the cancers into diagnostic categories.

Golub uses cDNA microarrays and a distinction calculation to identifygenes with expression behavior that distinguishes myeloid and lymphoidleukemias. See Golub et al. (1999) Science 286:531-7. Self organizingmaps were used for new class discovery. Cross validation was done with a“leave one out” analysis. 50 genes were identified as useful markers.This was reduced to as few as 10 genes with equivalent diagnosticaccuracy.

Hierarchical and non-hierarchical clustering methods are also useful foridentifying groups of genes that correlate with a subset of clinicalsamples such as with transplant rejection grade. Alizadeh usedhierarchical clustering as the primary tool to distinguish differenttypes of diffuse B-cell lymphomas based on gene expression profile data.See Alizadeh et al. (2000) Nature 403:503-11. Alizadeh used hierarchicalclustering as the primary tool to distinguish different types of diffuseB-cell lymphomas based on gene expression profile data. A cDNA arraycarrying 17856 probes was used for these experiments, 96 samples wereassessed on 128 arrays, and a set of 380 genes was identified as beinguseful for sample classification.

Perou demonstrates the use of hierarchical clustering for the molecularclassification of breast tumor samples based on expression profile data.See Perou et al. (2000) Nature 406:747-52. In this work, a cDNA arraycarrying 8102 gene probes was used. 1753 of these genes were found tohave high variation between breast tumors and were used for theanalysis.

Hastie describes the use of gene shaving for discovery of expressionmarkers. Hastie et al. (2000) Genome Biol. 1(2):RESEARCH 0003.1-0003.21.The gene shaving algorithm identifies sets of genes with similar orcoherent expression patterns, but large variation across conditions (RNAsamples, sample classes, patient classes). In this manner, genes with atight expression pattern within a transplant rejection grade, but alsowith high variability across rejection grades are grouped together. Thealgorithm takes advantage of both characteristics in one grouping step.For example, gene shaving can identify useful marker genes withco-regulated expression. Sets of useful marker genes can be reduced to asmaller set, with each gene providing some non-redundant value inclassification. This algorithm was used on the data set described inAlizadeh et al., supra, and the set of 380 informative gene markers wasreduced to 234.

Supervised harvesting of expression trees (Hastie 2001) identifies genesor clusters that best distinguish one class from all the others on thedata set. The method is used to identify the genes/clusters that canbest separate one class versus all the others for datasets that includetwo or more classes or all classes from each other. This algorithm canbe used for discovery or testing of a diagnostic gene set.

CART is a decision tree classification algorithm (Breiman 1984). Fromgene expression and or other data, CART can develop a decision tree forthe classification of samples. Each node on the decision tree involves aquery about the expression level of one or more genes or variables.Samples that are above the threshold go down one branch of the decisiontree and samples that are not go down the other branch. See examples 10and 16 for further description of its use in classification analysis andexamples of its usefulness in discovering and implementing a diagnosticgene set. CART identifies surrogates for each splitter (genes that arethe next best substitute for a useful gene inclassification.

Once a set of genes and expression criteria for those genes have beenestablished for classification, cross validation is done. There are manyapproaches, including a 10 fold cross validation analysis in which 10%of the training samples are left out of the analysis and theclassification algorithm is built with the remaining 90%. The 10% arethen used as a test set for the algorithm. The process is repeated 10times with 10% of the samples being left out as a test set each time.Through this analysis, one can derive a cross validation error whichhelps estimate the robustness of the algorithm for use on prospective(test) samples.

Clinical data are gathered for every patient sample used for expressionanalysis. Clinical variables can be quantitative or non-quantitative. Aclinical variable that is quantitiative can be used as a variable forsignificance or classification analysis. Non-quantitative clinicalvariables, such as the sex of the patient, can also be used in asignificance analysis or classification analysis with some statisticaltool. It is appreciated that the most useful diagnostic gene set for acondition may be optimal when considered along with one or morepredictive clinical variables. Clinical data can also be used assupervising vectors for a correlation analysis. That is to say that theclinical data associated with each sample can be used to divide thesamples into meaningful diagnostic categories for analysis. For example,samples can be divided into 2 or more groups based on the presence orabsence of some diagnostic criterion (a). In addition, clinical data canbe utilized to select patients for a correlation analysis or to excludethem based on some undesirable characteristic, such as an ongoinginfection, a medicine or some other issue. Clincial data can also beused to assess the pre-test probability of an outcome. For example,patients who are female are much more likely to be diagnosed as havingsystemic lupus erythematosis than patients who are male.

Once a set of genes are identified that classify samples with acceptableaccuracy. These genes are validated as a set using new samples that werenot used to discover the gene set. These samples can be taken fromfrozen archieves from the discovery clinical study or can be taken fromnew patients prospectively. Validation using a “test set” of samples canbe done using expression profiling of the gene set with microarrays orusing real-time PCR for each gene on the test set samples.Alternatively, a different expression profiling technology can be used.

Selected Diseases

In principle, diagnostic nucleotide sets of the invention may bedeveloped and applied to essentially any disease, or disease criterion,as long as at least one subset of nucleotide sequences is differentiallyexpressed in samples derived from one or more individuals with a diseasecriteria or disease and one or more individuals without the diseasecriteria or disease, wherein the individual may be the same individualsampled at different points in time, or the individuals may be differentindividuals (or populations of individuals). For example, the subset ofnucleotide sequences may be differentially expressed in the sampledtissues of subjects with the disease or disease criterion (e.g., apatient with a disease or disease criteria) as compared to subjectswithout the disease or disease criterion (e.g., patients without adisease (control patients)). Alternatively, or in addition, the subsetof nucleotide sequence(s) may be differentially expressed in differentsamples taken from the same patient, e.g at different points in time, atdifferent disease stages, before and after a treatment, in the presenceor absence of a risk factor, etc. Expression profiles corresponding tosets of nucleotide sequences that correlate not with a diagnosis, butrather with a particular aspect of a disease can also be used toidentify the diagnostic nucleotide sets and disease specific targetnucleotide sequences of the invention. For example, such an aspect, ordisease criterion, can relate to a subject's medical or family history,e.g., childhood illness, cause of death of a parent or other relative,prior surgery or other intervention, medications, symptoms (includingonset and/or duration of symptoms), etc. Alternatively, the diseasecriterion can relate to a diagnosis, e.g., hypertension, diabetes,atherosclerosis, or prognosis (e.g., prediction of future diagnoses,events or complications), e.g., acute myocardial infarction, restenosisfollowing angioplasty, reperfusion injury, allograft rejection,rheumatoid arthritis or systemic lupus erythematosis disease activity orthe like. In other cases, the disease criterion corresponds to atherapeutic outcome, e.g., transplant rejection, bypass surgery orresponse to a medication, restenosis after stent implantation,collateral vessel growth due to therapeutic angiogenesis therapy,decreased angina due to revascularization, resolution of symptomsassociated with a myriad of therapies, and the like. Alternatively, thedisease criteria corresponds with previously identified or classic riskfactors and may correspond to prognosis or future disease diagnosis. Asindicated above, a disease criterion can also correspond to genotype forone or more loci. Disease criteria (including patient data) may becollected (and compared) from the same patient at different points intime, from different patients, between patients with a disease(criterion) and patients respresenting a control population, etc.Longitudinal data, i.e., data collected at different time points from anindividual (or group of individuals) may be used for comparisons ofsamples obtained from an individual (group of individuals) at differentpoints in time, to permit identification of differences specificallyrelated to the disease state, and to obtain information relating to thechange in expression over time, including a rate of change or trajectoryof expression over time. The usefulness of longitudinal data is furtherdiscussed in the section titled “Identification of diagnostic nucleotidesets of the invention”.

It is further understood that diagnostic nucleotide sets may bedeveloped for use in diagnosing conditions for which there is no presentmeans of diagnosis. For example, in rheumatoid arthritis, jointdestruction is often well under way before a patient experience symptomsof the condition. A diagnostic nucleotide set may be developed thatdiagnoses rheumatic joint destruction at an earlier stage than would bepossible using present means of diagnosis, which rely in part on thepresentation of symptoms by a patient. Diagnostic nucleotide sets mayalso be developed to replace or augment current diagnostic procedures.For example, the use of a diagnostic nucleotide set to diagnose cardiacallograft rejection may replace the current diagnostic test, a graftbiopsy.

It is understood that the following discussion of diseases is exemplaryand non-limiting, and further that the general criteria discussed above,e.g. use of family medical history, are generally applicable to thespecific diseases discussed below.

In addition to leukocytes, as described throughout, the general methodis applicable to nucleotide sequences that are differentially expressedin any subject tissue or cell type, by the collection and assessment ofsamples of that tissue or cell type. However, in many cases, collectionof such samples presents significant technical or medical problems giventhe current state of the art.

Organ Transplant Rejection and Success

A frequent complication of organ transplantation is recognition of thetransplanted organ as foreign by the immune system resulting inrejection. Diagnostic nucleotide sets can be identified and validatedfor monitoring organ transplant success, rejection and treatment.Medications currently exist that suppress the immune system, and therebydecrease the rate of and severity of rejection. However, these drugsalso suppress the physiologic immune responses, leaving the patientsusceptible to a wide variety of opportunistic infections and cancers.At present there is no easy, reliable way to diagnose transplantrejection. Organ biopsy is the preferred method, but this is expensive,painful and associated with significant risk and has inadequatesensitivity for focal rejection.

Diagnostic nucleotide sets of the present invention can be developed andvalidated for use as diagnostic tests for transplant rejection andsuccess. It is appreciated that the methods of identifying diagnosticnucleotide sets are applicable to any organ transplant population. Forexample, diagnostic nucleotide sets are developed for cardiac allograftrejection and success.

In some cases, disease criteria correspond to acute stage rejectiondiagnosis based on organ biopsy and graded using the InternationalSociety for Heart and Lung Transplantation (“ISHLT”) criteria. Thisgrading system classifies endomyocardial biopsies on the histologicallevel as Grade 0, 1A, 1B, 2, 3A, 3B, or 4. Grade 0 biopies have noevidence of rejection, while each successive grade has increasedseverity of leukocyte infiltration and/or damage to the graft myocardialcells. It is appreciated that there is variability in the Gradingsystems between medical centers and pathologists and between repeatedreadings of the same pathologist at different times. When using thebiopsy grade as a disease criterion for leukocyte gene expressioncorrelation analysis, it may be desirable to have a single pathologistread all biopsy slides or have multiple pathologists read all slides todetermine the variablility in this disease criterion. It is alsoappreciated that cardiac biopsy, in part due to variability, is not 100%sensitive or 100% specific for diagnosing acute rejection. When usingthe cardiac biopsy grade as a disease criterion for the discovery ofdiagnostic gene sets, it may be desirable to divide patient samples intodiagnostic categories based on the grades. Examples of such classes arethose patients with: Grade 0 vs. Grades 1A-4, Grade 0 vs. Grades 1B-4,Grade 0 vs. Grades 2-4, Grade 0-1 vs. Grade 2-4, Grade 0-1 vs. Grade3A-4, or Grade 0 vs. Grade 3A-4.

Other disease criteria correspond to the cardiac biopsy results andother criteria, such as the results of cardiac function testing byechocardiography, hemodynamics assessment by cardiac catheterization,CMV infection, weeks post transplant, medication regimen, demographicsand/or results of other diagnostic tests.

Other disease criteria correspond to information from the patient'smedical history and information regarding the organ donor.Alternatively, disease criteria include the presence or absence ofcytomegalovirus (CMV) infection, Epstein-Barr virus (EBV) infection,allograft dysfunction measured by physiological tests of cardiacfunction (e.g., hemodynamic measurements from catheterization orechocardiograph data), and symptoms of other infections. Alternatively,disease criteria correspond to therapeutic outcome, e.g. graft failure,re-transplantation, death, hospitalization, need for intravenousimmunosuppression, transplant vasculopathy, response toimmunosuppressive medications, etc. Disease criteria may furthercorrespond to a rejection episode of at least moderate histologic grade,which results in treatment of the patient with additionalcorticosteroids, anti-T cell antibodies, or total lymphoid irradiation;a rejection with histologic grade 2 or higher; a rejection withhistologic grade <2; the absence of histologic rejection and normal orunchanged allograft function (based on hemodynamic measurements fromcatheterization or on echocardiographic data); the presence of severeallograft dysfunction or worsening allograft dysfunction during thestudy period (based on hemodynamic measurements from catheterization oron echocardiographic data); documented CMV infection by culture,histology, or PCR, and at least one clinical sign or symptom ofinfection; specific graft biopsy rejection grades; rejection of mild tomoderate histologic severity prompting augmentation of the patient'schronic immunosuppressive regimen; rejection of mild to moderateseverity with allograft dysfunction prompting plasmaphoresis or adiagnosis of “humoral” rejection; infections other than CMV, especiallyinfection with Epstein Barr virus (EBV); lymphoproliferative disorder(also called post-transplant lymphoma); transplant vasculopathydiagnosed by increased intimal thickness on intravascular ultrasound(IVUS), angiography, or acute myocardial infarction; graft failure orretransplantation; and all cause mortality. Further specific examples ofclinical data useful as disease criteria are provided in Example 9.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and treatment of kidney allograftrecipients. Disease criteria correspond to, e.g., results of biopsyanalysis for kidney allograft rejection, serum creatine level,creatinine clearance, radiological imaging results for the kidney andurinalysis results. Another disease criterion corresponds to the needfor hemodialysis, retransplantation, death or other renal replacementtherapy. Diagnostic nucleotide sets are developed and validated for usein diagnosis and treatment of bone marrow transplant and livertransplantation pateints, respectively. Disease criteria for bone marrowtransplant correspond to the diagnosis and monitoring of graft rejectionand/or graft versus host disease, the recurrence of cancer,complications due to immunosuppression, hematologic abnormalities,infection, hospitalization and/or death. Disease criteria for livertransplant rejection include levels of serum markers for liver damageand liver function such as AST (aspartate aminotransferase), ALT(alanine aminotransferase), Alkaline phosphatase, GGT, (gamma-glutamyltranspeptidase) Bilirubin, Albumin and Prothrombin time. Further diseasecriteria correspond to hepatic encephalopathy, medication usage,ascites, graft failure, retransplantation, hospitalization,complications of immunosuppression, results of diagnostic tests, resultsof radiological testing, death and histological rejection on graftbiopsy. In addition, urine can be utilized for at the target tissue forprofiling in renal transplant, while biliary and intestinal secretionsand feces may be used favorably for hepatic or intestinal organallograft rejection.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and treatment of xenograft recipients.This can include the transplantation of any organ from a non-humananimal to a human or between non-human animals. Considerations fordiscovery and application of diagnostics and therapeutics and fordisease criterion are substantially similar to those for allografttransplantation between humans.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and treatment of artificial organrecipients. This includes, but is not limited to mechanical circulatorysupport, artificial hearts, left ventricular assist devices, renalreplacement therapies, organ prostheses and the like. Disease criteriaare thrombosis (blood clots), infection, death, hospitalization, andworsening measures of organ function (e.g., hemodynamics, creatinine,liver function testing, renal function testing, functional capacity).

In another example, diagnostic nucleotide sets are developed andvalidated for use in matching donor organs to appropriate recipients.Diagnostic gene set can be discovered that correlate with successfulmatching of donor organ to recipient. Disease criteria include graftfailure, acute and chronic rejection, death, hospitalization,immunosuppressive drug use, and complications of immunosuppression. Genesets may be assayed from the donor or recipient's peripheral blood,organ tissue or some other tissue.

In another example, diagnostic nucleotide sets are developed andvalidated for use in diagnosis and induction of patient immune tolerance(decrease rejection of an allograft by the host immune system). Diseasecriteria include rejection, assays of immune activation, need forimmunosupression and all disease criteria noted above fortransplantation of each organ.

Viral Diseases

Diagnostic leukocyte nucleotide sets may be developed and validated foruse in diagnosing viral disease. In another aspect, viral nucleotidesequences may be added to a leukocyte nucleotide set for use indiagnosis of viral diseases. Alternatively, viral nucleotide sets andleukocyte nucleotides sets may be used sequentially.

Epstein-Ban Virus (EBV)

EBV causes a variety of diseases such as mononucleosis, B-cell lymphoma,and pharyngeal carcinoma. It infects mononuclear cells and circulatingatypical lymphocytes are a common manifestation of infection. Peripheralleukocyte gene expression is altered by infection. Transplant recipientsand patients who are immunosuppressed are at increased risk forEBV-associated lymphoma.

Diagnostic nucleotide sets may be developed and validated for use indiagnosis and monitoring of EBV. In one aspect, the diagnosticnucleotide set is a leukocyte nucleotide set. Alternatively, EBVnucleotide sequences are added to a leukocyte nucleotide set, for use indiagnosing EBV. Disease criteria correspond with diagnosis of EBV, and,in patients who are EBV-sero-positive, presence (or prospectiveoccurrence) of EBV-related illnesses such as mononucleosis, andEBV-associated lymphoma. Diagnostic nucleotide sets are useful fordiagnosis of EBV, and prediction of occurrence of EBV-related illnesses.

Cytomegalovirus (CMV)

Cytomegalovirus cause inflammation and disease in almost any tissue,particularly the colon, lung, bone marrow and retina, and is a veryimportant cause of disease in immunosuppressed patients, e.g.transplant, cancer, AIDS. Many patients are infected with or have beenexposed to CMV, but not all patients develop clinical disease from thevirus. Also, CMV negative recipients of allografts that come from CMVpositive donors are at high risk for CMV infection. As immunosuppressivedrugs are developed and used, it is increasingly important to identifypatients with current or impending clinical CMV disease, because thepotential benefit of immunosuppressive therapy must be balanced with theincreased rate of clinical CMV infection and disease that may resultfrom the use of immunosuppression therapy. CMV may also play a role inthe occurrence of atherosclerosis or restenosis after angioplasty.

Diagnostic nucleotide sets are developed for use in diagnosis andmonitoring of CMV infection or re-activation of CMV infection. In oneaspect, the diagnostic nucleotide set is a leukocyte nucleotide set. Inanother aspect, CMV nucleotide sequences are added to a leukocytenucleotide set, for use in diagnosing CMV. Disease criteria correspondto diagnosis of CMV (e.g., sero-positive state) and presence ofclinically active CMV. Disease criteria may also correspond toprospective data, e.g. the likelihood that CMV will become clinicallyactive or impending clinical CMV infection. Antiviral medications areavailable and diagnostic nucleotide sets can be used to select patientsfor early treatment, chronic suppression or prophylaxis of CMV activity.

Hepatitis B and C

These chronic viral infections affect about 1.25 and 2.7 millionpatients in the US, respectively. Many patients are infected, but sufferno clinical manifestations. Some patients with infection go on to sufferfrom chronic liver failure, cirrhosis and hepatic carcinoma.

Diagnostic nucleotide sets are developed for use in diagnosis andmonitoring of HBV or HCV infection. In one aspect, the diagnosticnucleotide set is a leukocyte nucleotide set. In another aspect, viralnucleotide sequences are added to a leukocyte nucleotide set, for use indiagnosing the virus and monitoring progression of liver disease.Disease criteria correspond to diagnosis of the virus (e.g.,sero-positive state or other disease symptoms). Alternatively, diseasecriteria correspond to liver damage, e.g., elevated alkalinephosphatase, ALT, AST or evidence of ongoing hepatic damage on liverbiopsy. Alternatively, disease criteria correspond to serum liver tests(AST, ALT, Alkaline Phosphatase, GGT, PT, bilirubin), liver biopsy,liver ultrasound, viral load by serum PCR, cirrhosis, hepatic cancer,need for hospitalization or listing for liver transplant. Diagnosticnucleotide sets are used to diagnose HBV and HCV, and to predictlikelihood of disease progression. Antiviral therapeutic usage, such asInterferon gamma and Ribavirin, can also be disease criteria.

HIV

HIV infects T cells and certainly causes alterations in leukocyteexpression. Diagnostic nucleotide sets are developed for diagnosis andmonitoring of HIV. In one aspect, the diagnostic nucleotide set is aleukocyte nucleotide set. In another aspect, viral nucleotide sequencesare added to a leukocyte nucleotide set, for use in diagnosing thevirus. Disease criteria correspond to diagnosis of the virus (e.g.,sero-positive state). In addition, disease criteria correspond to viralload, CD4 T cell counts, opportunistic infection, response toantiretroviral therapy, progression to AIDS, rate of progression and theoccurrence of other HIV related outcomes (e.g., malignancy, CNSdisturbance). Response to antiretrovirals may also be disease criteria.

Pharmacogenomics

Pharmocogenomics is the study of the individual propensity to respond toa particular drug therapy (combination of therapies). In this context,response can mean whether a particular drug will work on a particularpatient, e.g. some patients respond to one drug but not to another drug.Response can also refer to the likelihood of successful treatment or theassessment of progress in treatment. Titration of drug therapy to aparticular patient is also included in this description, e.g. differentpatients can respond to different doses of a given medication. Thisaspect may be important when drugs with side-effects or interactionswith other drug therapies are contemplated.

Diagnostic nucleotide sets are developed and validated for use inassessing whether a patient will respond to a particular therapy and/ormonitoring response of a patient to drug therapy (therapies). Diseasecriteria correspond to presence or absence of clinical symptoms orclinical endpoints, presence of side-effects or interaction with otherdrug(s). The diagnostic nucleotide set may further comprise nucleotidesequences that are targets of drug treatment or markers of activedisease.

Validation and Accuracy of Diagnostic Nucleotide Sets

Prior to widespread application of the diagnostic probe sets of theinvention the predictive value of the probe set is validated. When thediagnostic probe set is discovered by microarray based expressionanalysis, the differential expression of the member genes may bevalidated by a less variable and more quantitive and accurate technologysuch as real time PCR. In this type of experiment the amplificationproduct is measured during the PCR reaction. This enables the researcherto observe the amplification before any reagent becomes rate limitingfor amplification. In kinetic PCR the measurement is of C_(T) (thresholdcycle) or C_(P) (crossing point). This measurement (C_(T)=C_(P)) is thepoint at which an amplification curve crosses a threshold fluorescencevalue. The threshold is set to a point within the area where all of thereactions were in their linear phase of amplification. When measuringC_(T), a lower C_(T) value is indicative of a higher amount of startingmaterial since an earlier cycle number means the threshold was crossedmore quickly.

Several fluorescence methodologies are available to measureamplification product in real-time PCR. Taqman (Applied BioSystems,Foster City, Calif.) uses fluorescence resonance energy transfer (FRET)to inhibit signal from a probe until the probe is degraded by thesequence specific binding and Taq 3′ exonuclease activity. MolecularBeacons (Stratagene, La Jolla, Calif.) also use FRET technology, wherebythe fluorescence is measured when a hairpin structure is relaxed by thespecific probe binding to the amplified DNA. The third commonly usedchemistry is Sybr Green, a DNA-binding dye (Molecular Probes, Eugene,Oreg.). The more amplified product that is produced, the higher thesignal. The Sybr Green method is sensitive to non-specific amplificationproducts, increasing the importance of primer design and selection.Other detection chemistries can also been used, such as ethedium bromideor other DNA-binding dyes and many modifications of the fluorescentdye/quencher dye Taqman chemistry, for example scorpions.

Real-time PCR validation can be done as described in Example 15.

Typically, the oligonucleotide sequence of each probe is confirmed, e.g.by DNA sequencing using an oligonucleotide-specific primer. Partialsequence obtained is generally sufficient to confirm the identity of theoligonucleotide probe. Alternatively, a complementary polynucleotide isfluorescently labeled and hybridized to the array, or to a differentarray containing a resynthesized version of the oligo nucleotide probe,and detection of the correct probe is confirmed.

Typically, validation is performed by statistically evaluating theaccuracy of the correspondence between the molecular signature for adiagnostic probe set and a selected indicator. For example, theexpression differential for a nucleotide sequence between two subjectclasses can be expressed as a simple ratio of relative expression. Theexpression of the nucleotide sequence in subjects with selectedindicator can be compared to the expression of that nucleotide sequencein subjects without the indicator, as described in the followingequations.

ΣE _(x) ai/N=E _(x) A the average expression of nucleotide sequence x inthe members of group A;

ΣE _(x) bi/M=E _(x) B the average expression of nucleotide sequence x inthe members of group B;

E _(X) A/ExB=ΔE _(x) AB the average differential expression ofnucleotide sequence x between groups A and B:

where Σ indicates a sum; Ex is the expression of nucleotide sequence xrelative to a standard; ai are the individual members of group A, groupA has N members; bi are the individual members of group B, group B has Mmembers.

The expression of at least two nucleotide sequences, e.g., nucleotidesequence X and nucleotide sequence Y are measured relative to a standardin at least one subject of group A (e.g., with a disease) and group B(e.g., without the disease). Ideally, for purposes of validation theindicator is independent from (i.e., not assigned based upon) theexpression pattern. Alternatively, a minimum threshold of geneexpression for nucleotide sequences X and Y, relative to the standard,are designated for assignment to group A. For nucleotide sequence x,this threshold is designated ΔEx, and for nucleotide sequence y, thethreshold is designated ΔEy.

The following formulas are used in the calculations below:

Sensitivity=(true positives/true positives+false negatives)

Specificity=(true negatives/true negatives+false positives)

If, for example, expression of nucleotide sequence x above a threshold:x>ΔEx, is observed for 80/100 subjects in group A and for 10/100subjects in group B, the sensitivity of nucleotide sequence x for theassignment to group A, at the given expression threshold ΔEx, is 80%,and the specificity is 90%.

If the expression of nucleotide sequence y is >ΔEy in 80/100 subjects ingroup A, and in 10/100 subjects in group B, then, similarly thesensitivity of nucleotide sequence y for the assignment to group A atthe given threshold ΔEy is 80% and the specificity is 90%. If inaddition, 60 of the 80 subjects in group A that meet the expressionthreshold for nucleotide sequence y also meet the expression thresholdΔEx and that 5 of the 10 subjects in group B that meet the expressionthreshold for nucleotide sequence y also meet the expression thresholdΔEx, the sensitivity of the test (x>ΔEx and y>ΔEy) for assignment ofsubjects to group A is 60% and the specificity is 95%.

Alternatively, if the criteria for assignment to group A are change to:Expression of x>ΔEx or expression of y>ΔEy, the sensitivity approaches100% and the specificity is 85%.

Clearly, the predictive accuracy of any diagnostic probe set isdependent on the minimum expression threshold selected. The expressionof nucleotide sequence X (relative to a standard) is measured insubjects of groups A (with disease) and B (without disease). The minimumthreshold of nucleotide sequence expression for x, required forassignment to group A is designated ΔEx 1.

If 90/100 patients in group A have expression of nucleotide sequencex>ΔEx 1 and 20/100 patients in group B have expression of nucleotidesequence x>ΔEx 1, then the sensitivity of the expression of nucleotidesequence x (using ΔEx 1 as a minimum expression threshold) forassignment of patients to group A will be 90% and the specificity willbe 80%.

Altering the minimum expression threshold results in an alteration inthe specificity and sensitivity of the nucleotide sequences in question.For example, if the minimum expression threshold of nucleotide sequencex for assignment of subjects to group A is lowered to ΔEx 2, such that100/100 subjects in group A and 40/100 subjects in group B meet thethreshold, then the sensitivity of the test for assignment of subjectsto group A will be 100% and the specificity will be 60%.

Thus, for 2 nucleotide sequences X and Y: the expression of nucleotidesequence x and nucleotide sequence y (relative to a standard) aremeasured in subjects belonging to groups A (with disease) and B (withoutdisease). Minimum thresholds of nucleotide sequence expression fornucleotide sequences X and Y (relative to common standards) aredesignated for assignment to group A. For nucleotide sequence x, thisthreshold is designated ΔEx1 and for nucleotide sequence y, thisthreshold is designated ΔEy1.

If in group A, 90/100 patients meet the minimum requirements ofexpression ΔEx1 and ΔEy1, and in group B, 10/100 subjects meet theminimum requirements of expression ΔEx1 and ΔEy1, then the sensitivityof the test for assignment of subjects to group A is 90% and thespecificity is 90%.

Increasing the minimum expression thresholds for X and Y to ΔEx2 andΔEy2, such that in group A, 70/100 subjects meet the minimumrequirements of expression ΔEx2 and ΔEy2, and in group B, 3/100 subjectsmeet the minimum requirements of expression ΔEx2 and ΔEy2. Now thesensitivity of the test for assignment of subjects to group A is 70% andthe specificity is 97%.

If the criteria for assignment to group A is that the subject inquestion meets either threshold, ΔEx2 or ΔEy2, and it is found that100/100 subjects in group A meet the criteria and 20/100 subjects ingroup B meet the criteria, then the sensitivity of the test forassignment to group A is 100% and the specificity is 80%.

Individual components of a diagnostic probe set each have a definedsensitivity and specificity for distinguishing between subject groups.Such individual nucleotide sequences can be employed in concert as adiagnostic probe set to increase the sensitivity and specificity of theevaluation. The database of molecular signatures is queried byalgorithms to identify the set of nucleotide sequences (i.e.,corresponding to members of the probe set) with the highest averagedifferential expression between subject groups. Typically, as the numberof nucleotide sequences in the diagnostic probe set increases, so doesthe predictive value, that is, the sensitivity and specificity of theprobe set. When the probe sets are defined they may be used fordiagnosis and patient monitoring as discussed below. The diagnosticsensitivity and specificity of the probe sets for the defined use can bedetermined for a given probe set with specified expression levels asdemonstrated above. By altering the expression threshold required forthe use of each nucleotide sequence as a diagnostic, the sensitivity andspecificity of the probe set can be altered by the practitioner. Forexample, by lowering the magnitude of the expression differentialthreshold for each nucleotide sequence in the set, the sensitivity ofthe test will increase, but the specificity will decrease. As isapparent from the foregoing discussion, sensitivity and specificity areinversely related and the predictive accuracy of the probe set iscontinuous and dependent on the expression threshold set for eachnucleotide sequence. Although sensitivity and specificity tend to havean inverse relationship when expression thresholds are altered, bothparameters can be increased as nucleotide sequences with predictivevalue are added to the diagnostic nucleotide set. In addition a singleor a few markers may not be reliable expression markers across apopulation of patients. This is because of the variability in expressionand measurement of expression that exists between measurements,individuals and individuals over time. Inclusion of a large number ofcandidate nucleotide sequences or large numbers of nucleotide sequencesin a diagnostic nucleotide set allows for this variability as not allnucleotide sequences need to meet a threshold for diagnosis. Generally,more markers are better than a single marker. If many markers are usedto make a diagnosis, the likelihood that all expression markers will notmeet some thresholds based upon random variability is low and thus thetest will give fewer false negatives.

It is appreciated that the desired diagnostic sensitivity andspecificity of the diagnostic nucleotide set may vary depending on theintended use of the set. For example, in certain uses, high specificityand high sensitivity are desired. For example, a diagnostic nucleotideset for predicting which patient population may experience side effectsmay require high sensitivity so as to avoid treating such patients. Inother settings, high sensitivity is desired, while reduced specificitymay be tolerated. For example, in the case of a beneficial treatmentwith few side effects, it may be important to identify as many patientsas possible (high sensitivity) who will respond to the drug, andtreatment of some patients who will not respond is tolerated. In othersettings, high specificity is desired and reduced sensitivity may betolerated. For example, when identifying patients for an early-phaseclinical trial, it is important to identify patients who may respond tothe particular treatment. Lower sensitivity is tolerated in this settingas it merely results in reduced patients who enroll in the study orrequires that more patients are screened for enrollment.

Methods of Using Diagnostic Nucleotide Sets.

The invention also provide methods of using the diagnostic nucleotidesets to: diagnose disease; assess severity of disease; predict futureoccurrence of disease; predict future complications of disease;determine disease prognosis; evaluate the patient's risk, or “stratify”a group of patients; assess response to current drug therapy; assessresponse to current non-pharmacological therapy; determine the mostappropriate medication or treatment for the patient; predict whether apatient is likely to respond to a particular drug; and determine mostappropriate additional diagnostic testing for the patient, among otherclinically and epidemiologically relevant applications.

The nucleotide sets of the invention can be utilized for a variety ofpurposes by physicians, healthcare workers, hospitals, laboratories,patients, companies and other institutions. As indicated previously,essentially any disease, condition, or status for which at least onenucleotide sequence is differentially expressed in leukocyte populations(or sub-populations) can be evaluated, e.g., diagnosed, monitored, etc.using the diagnostic nucleotide sets and methods of the invention. Inaddition to assessing health status at an individual level, thediagnostic nucleotide sets of the present invention are suitable forevaluating subjects at a “population level,” e.g., for epidemiologicalstudies, or for population screening for a condition or disease.

Collection and Preparation of Sample

RNA, protein and/or DNA is prepared using methods well-known in the art,as further described herein. It is appreciated that subject samplescollected for use in the methods of the invention are generallycollected in a clinical setting, where delays may be introduced beforeRNA samples are prepared from the subject samples of whole blood, e.g.the blood sample may not be promptly delivered to the clinical lab forfurther processing. Further delay may be introduced in the clinical labsetting where multiple samples are generally being processed at anygiven time. For this reason, methods which feature lengthy incubationsof intact leukocytes at room temperature are not preferred, because theexpression profile of the leukocytes may change during this extendedtime period. For example, RNA can be isolated from whole blood using aphenol/guanidine isothiocyanate reagent or another direct whole-bloodlysis method, as described in, e.g., U.S. Pat. Nos. 5,346,994 and4,843,155. This method may be less preferred under certain circumstancesbecause the large majority of the RNA recovered from whole blood RNAextraction comes from erythrocytes since these cells outnumberleukocytes 1000:1. Care must be taken to ensure that the presence oferythrocyte RNA and protein does not introduce bias in the RNAexpression profile data or lead to inadequate sensitivity or specificityof probes.

Alternatively, intact leukocytes may be collected from whole blood usinga lysis buffer that selectively lyses erythrocytes, but not leukocytes,as described, e.g., in (U.S. Pat. Nos. 5,973,137, and 6,020,186). Intactleukocytes are then collected by centrifugation, and leukocyte RNA isisolated using standard protocols, as described herein. However, thismethod does not allow isolation of sub-populations of leukocytes, e.g.mononuclear cells, which may be desired. In addition, the expressionprofile may change during the lengthy incubation in lysis buffer,especially in a busy clinical lab where large numbers of samples arebeing prepared at any given time.

Alternatively, specific leukocyte cell types can be separated usingdensity gradient reagents (Boyum, A, 1968.). For example, mononuclearcells may be separated from whole blood using density gradientcentrifugation, as described, e.g., in U.S. Pat. Nos. 4,190,535,4,350,593, 4,751,001, 4,818,418, and 5053134. Blood is drawn directlyinto a tube containing an anticoagulant and a density reagent (such asFicoll or Percoll). Centrifugation of this tube results in separation ofblood into an erythrocyte and granulocyte layer, a mononuclear cellsuspension, and a plasma layer. The mononuclear cell layer is easilyremoved and the cells can be collected by centrifugation, lysed, andfrozen. Frozen samples are stable until RNA can be isolated. Densitycentrifugation, however, must be conducted at room temperature, and ifprocessing is unduly lengthy, such as in a busy clinical lab, theexpression profile may change.

The quality and quantity of each clinical RNA sample is desirablychecked before amplification and labeling for array hybridization, usingmethods known in the art. For example, one microliter of each sample maybe analyzed on a Bioanalyzer (Agilent 2100 Palo Alto, Calif. USA) usingan RNA 6000 nano LabChip (Caliper, Mountain View, Calif. USA). DegradedRNA is identified by the reduction of the 28S to 18S ribosomal RNA ratioand/or the presence of large quantities of RNA in the 25-100 nucleotiderange.

It is appreciated that the RNA sample for use with a diagnosticnucleotide set may be produced from the same or a different cellpopulation, sub-population and/or cell type as used to identify thediagnostic nucleotide set. For example, a diagnostic nucleotide setidentified using RNA extracted from mononuclear cells may be suitablefor analysis of RNA extracted from whole blood or mononuclear cells,depending on the particular characteristics of the members of thediagnostic nucleotide set. Generally, diagnostic nucleotide sets must betested and validated when used with RNA derived from a different cellpopulation, sub-population or cell type than that used when obtainingthe diagnostic gene set. Factors such as the cell-specific geneexpression of diagnostic nucleotide set members, redundancy of theinformation provided by members of the diagnostic nucleotide set,expression level of the member of the diagnostic nucleotide set, andcell-specific alteration of expression of a member of the diagnosticnucleotide set will contribute to the usefullness of using a differentRNA source than that used when identifying the members of the diagnosticnucleotide set. It is appreciated that it may be desirable to assay RNAderived from whole blood, obviating the need to isolate particular celltypes from the blood.

Rapid Method of RNA Extraction Suitable for Production in a ClinicalSetting of High Quality RNA for Expression Profiling

In a clinical setting, obtaining high quality RNA preparations suitablefor expression profiling, from a desired population of leukocytes posescertain technical challenges, including: the lack of capacity for rapid,high-throughput sample processing in the clinical setting, and thepossibility that delay in processing (in a busy lab or in the clinicalsetting) may adversely affect RNA quality, e.g. by a permitting theexpression profile of certain nucleotide sequences to shift. Also, useof toxic and expensive reagents, such as phenol, may be disfavored inthe clinical setting due to the added expense associated with shippingand handling such reagents.

A useful method for RNA isolation for leukocyte expression profilingwould allow the isolation of monocyte and lymphocyte RNA in a timelymanner, while preserving the expression profiles of the cells, andallowing inexpensive production of reproducible high-quality RNAsamples. Accordingly, the invention provides a method of addinginhibitor(s) of RNA transcription and/or inhibitor(s) of proteinsynthesis, such that the expression profile is “frozen” and RNAdegradation is reduced. A desired leukocyte population or sub-populationis then isolated, and the sample may be frozen or lysed before furtherprocessing to extract the RNA. Blood is drawn from subject populationand exposed to ActinomycinD (to a final concentration of 10 ug/ml) toinhibit transcription, and cycloheximide (to a final concentration of 10ug/ml) to inhibit protein synthesis. The inhibitor(s) can be injectedinto the blood collection tube in liquid form as soon as the blood isdrawn, or the tube can be manufactured to contain either lyophilizedinhibitors or inhibitors that are in solution with the anticoagulant. Atthis point, the blood sample can be stored at room temperature until thedesired leukocyte population or sub-population is isolated, as describedelsewhere. RNA is isolated using standard methods, e.g., as describedabove, or a cell pellet or extract can be frozen until furtherprocessing of RNA is convenient.

The invention also provides a method of using a low-temperature densitygradient for separation of a desired leukocyte sample. In anotherembodiment, the invention provides the combination of use of alow-temperature density gradient and the use of transcriptional and/orprotein synthesis inhibitor(s). A desired leukocyte population isseparated using a density gradient solution for cell separation thatmaintains the required density and viscosity for cell separation at 0-4°C. Blood is drawn into a tube containing this solution and may berefrigerated before and during processing as the low temperatures slowcellular processes and minimize expression profile changes. Leukocytesare separated, and RNA is isolated using standard methods. Alternately,a cell pellet or extract is frozen until further processing of RNA isconvenient. Care must be taken to avoid rewarming the sample duringfurther processing steps.

Alternatively, the invention provides a method of using low-temperaturedensity gradient separation, combined with the use of actinomycin A andcyclohexamide, as described above.

Assessing Expression for Diagnostics

Expression profiles for the set of diagnostic nucleotide sequences in asubject sample can be evaluated by any technique that determines theexpression of each component nucleotide sequence. Methods suitable forexpression analysis are known in the art, and numerous examples arediscussed in the Sections titled “Methods of obtaining expression data”and “high throughput expression Assays”, above.

In many cases, evaluation of expression profiles is most efficiently,and cost effectively, performed by analyzing RNA expression.Alternatively, the proteins encoded by each component of the diagnosticnucleotide set are detected for diagnostic purposes by any techniquecapable of determining protein expression, e.g., as described above.Expression profiles can be assessed in subject leukocyte sample usingthe same or different techniques as those used to identify and validatethe diagnostic nucleotide set. For example, a diagnostic nucleotide setidentified as a subset of sequences on a cDNA microarray can be utilizedfor diagnostic (or prognostic, or monitoring, etc.) purposes on the samearray from which they were identified. Alternatively, the diagnosticnucleotide sets for a given disease or condition can be organized onto adedicated sub-array for the indicated purpose. It is important to notethat if diagnostic nucleotide sets are discovered using one technology,e.g. RNA expression profiling, but applied as a diagnostic using anothertechnology, e.g. protein expression profiling, the nucleotide sets mustgenerally be validated for diagnostic purposes with the new technology.In addition, it is appreciated that diagnostic nucleotide sets that aredeveloped for one use, e.g. to diagnose a particular disease, may laterbe found to be useful for a different application, e.g. to predict thelikelihood that the particular disease will occur. Generally, thediagnostic nucleotide set will need to be validated for use in thesecond circumstance. As discussed herein, the sequence of diagnosticnucleotide set members may be amplified from RNA or cDNA using methodsknown in the art providing specific amplification of the nucleotidesequences.

Identification of Novel Nucleotide Sequences that are DifferentiallyExpressed in Leukocytes

Novel nucleotide sequences that are differentially expressed inleukocytes are also part of the invention. Previously unidentified openreading frames may be identified in a library of differentiallyexpressed candidate nucleotide sequences, as described above, and theDNA and predicted protein sequence may be identified and characterizedas noted above. We identified unnamed (not previously described ascorresponding to a gene, or an expressed gene) nucleotide sequences inthe our candidate nucleotide library, depicted in Table 3A, 3B, Tables8, 11-12, 14 and the sequence listing. Accordingly, further embodimentsof the invention are the isolated nucleic acids described in Tables 3Aand 3B, and in the sequence listing. The novel differentially expressednucleotide sequences of the invention are useful in the diagnosticnucleotide set of the invention described above, and are further usefulas members of a diagnostic nucleotide set immobilized on an array. Thenovel partial nucleotide sequences may be further characterized usingsequence tools and publically or privately accessible sequencedatabases, as is well known in the art: Novel differentially expressednucleotide sequences may be identified as disease target nucleotidesequences, described below. Novel nucleotide sequences may also be usedas imaging reagent, as further described below.

As used herein, “nucleotide sequence” refers to (a) a nucleotidesequence containing at least one of the DNA sequences disclosed herein(as shown in FIGS. Table 3A, 3B, Tables 8, 11-12, 14 and the sequencelisting); (b) any DNA sequence that encodes the amino acid sequenceencoded by the DNA sequences disclosed herein; (c) any DNA sequence thathybridizes to the complement of the coding sequences disclosed herein,contained within the coding region of the nucleotide sequence to whichthe DNA sequences disclosed herein (as shown in Table 3A, 3B, Tables 8,11-12, 14 and the sequence listing) belong, under highly stringentconditions, e.g., hybridization to filter-bound DNA in 0.5 M NaHPO₄, 7%sodium dodecyl sulfate (SDS), 1 mM EDTA at 65° C., and washing in0.1×SSC/0.1% SDS at 68° C. (Ausubel F. M. et al., eds., 1989, CurrentProtocols in Molecular Biology, Vol. I, Green Publishing Associates,Inc., and John Wiley & sons, Inc., New York, at p. 2.10.3), (d) any DNAsequence that hybridizes to the complement of the coding sequencesdisclosed herein, (as shown in Table 3A, 3B, 8, 11-12, 14 and thesequence listing) contained within the coding region of the nucleotidesequence to which DNA sequences disclosed herein (as shown in TABLES 3A,3B, Tables 8, 11-12, 14) belong, under less stringent conditions, suchas moderately stringent conditions, e.g., washing in 0.2×SSC/0.1% SDS at42° C. (Ausubel et al., 1989, supra), yet which still encodes afunctionally equivalent gene product; and/or (e) any DNA sequence thatis at least 90% identical, at least 80% identical or at least 70%identical to the coding sequences disclosed herein (as shown in TABLES3A, 3B and the sequence listing), wherein % identity is determined usingstandard algorithms known in the art.

The invention also includes nucleic acid molecules, preferably DNAmolecules, that hybridize to, and are therefore the complements of, theDNA sequences (a) through (c), in the preceding paragraph. Suchhybridization conditions may be highly stringent or less highlystringent, as described above. In instances wherein the nucleic acidmolecules are deoxyoligonucleotides (“oligos”), highly stringentconditions may refer, e.g., to washing in 6×SSC/0.05% sodiumpyrophosphate at 37° C. (for 14-base oligos), 48° C. (for 17-baseoligos), 55° C. (for 20-base oligos), and 60° C. (for 23-base oligos).These nucleic acid molecules may act as target nucleotide sequenceantisense molecules, useful, for example, in target nucleotide sequenceregulation and/or as antisense primers in amplification reactions oftarget nucleotide sequence nucleic acid sequences. Further, suchsequences may be used as part of ribozyme and/or triple helix sequences,also useful for target nucleotide sequence regulation. Still further,such molecules may be used as components of diagnostic methods wherebythe presence of a disease-causing allele, may be detected.

The invention also encompasses nucleic acid molecules contained infull-length gene sequences that are related to or derived from sequencesin Tables 2, 3, Tables 8, 11-12, 14 and the sequence listing. Onesequence may map to more than one full-length gene.

The invention also encompasses (a) DNA vectors that contain any of theforegoing coding sequences and/or their complements (i.e., antisense);(b) DNA expression vectors that contain any of the foregoing codingsequences operatively associated with a regulatory element that directsthe expression of the coding sequences; and (c) genetically engineeredhost cells that contain any of the foregoing coding sequencesoperatively associated with a regulatory element that directs theexpression of the coding sequences in the host cell. As used herein,regulatory elements include but are not limited to inducible andnon-inducible promoters, enhancers, operators and other elements knownto those skilled in the art that drive and regulate expression. Theinvention includes fragments of any of the DNA sequences disclosedherein. Fragments of the DNA sequences may be at least 5, at least 10,at least 15, at least 19 nucleotides, at least 25 nucleotides, at least50 nucleotides, at least 100 nucleotides, at least 200, at least 500, orlarger.

In addition to the nucleotide sequences described above, homologues ofsuch sequences, as may, for example be present in other species, may beidentified and may be readily isolated, without undue experimentation,by molecular biological techniques well known in the art, as well as useof gene analysis tools described above, and e.g., in Example 4. Further,there may exist nucleotide sequences at other genetic loci within thegenome that encode proteins which have extensive homology to one or moredomains of such gene products. These nucleotide sequences may also beidentified via similar techniques.

For example, the isolated differentially expressed nucleotide sequencemay be labeled and used to screen a cDNA library constructed from mRNAobtained from the organism of interest. Hybridization conditions will beof a lower stringency when the cDNA library was derived from an organismdifferent from the type of organism from which the labeled sequence wasderived. Alternatively, the labeled fragment may be used to screen agenomic library derived from the organism of interest, again, usingappropriately stringent conditions. Such low stringency conditions willbe well known to those of skill in the art, and will vary predictablydepending on the specific organisms from which the library and thelabeled sequences are derived. For guidance regarding such conditionssee, for example, Sambrook et al., 1989, Molecular Cloning, A LaboratoryManual, Cold Springs Harbor Press, N.Y.; and Ausubel et al., 1989,Current Protocols in Molecular Biology, Green Publishing Associates andWiley Interscience, N.Y.

Novel nucleotide products include those proteins encoded by the novelnucleotide sequences described, above. Specifically, novel gene productsmay include polypeptides encoded by the novel nucleotide sequencescontained in the coding regions of the nucleotide sequences to which DNAsequences disclosed herein (in TABLES 3A, 3B, Tables 8, 11-12, 14 andthe sequence listing).

In addition, novel protein products of novel nucleotide sequences mayinclude proteins that represent functionally equivalent gene products.Such an equivalent novel gene product may contain deletions, additionsor substitutions of amino acid residues within the amino acid sequenceencoded by the novel nucleotide sequences described, above, but whichresult in a silent change, thus producing a functionally equivalentnovel nucleotide sequence product. Amino acid substitutions may be madeon the basis of similarity in polarity, charge, solubility,hydrophobicity, hydrophilicity, and/or the amphipathic nature of theresidues involved.

For example, nonpolar (hydrophobic) amino acids include alanine,leucine, isoleucine, valine, proline, phenylalanine, tryptophan, andmethionine; polar neutral amino acids include glycine, serine,threonine, cysteine, tyrosine, asparagine, and glutamine; positivelycharged (basic) amino acids include arginine, lysine, and histidine; andnegatively charged (acidic) amino acids include aspartic acid andglutamic acid. “Functionally equivalent”, as utilized herein, refers toa protein capable of exhibiting a substantially similar in vivo activityas the endogenous novel gene products encoded by the novel nucleotidedescribed, above.

The novel gene products (protein products of the novel nucleotidesequences) may be produced by recombinant DNA technology usingtechniques well known in the art. Thus, methods for preparing the novelgene polypeptides and peptides of the invention by expressing nucleicacid encoding novel nucleotide sequences are described herein. Methodswhich are well known to those skilled in the art can be used toconstruct expression vectors containing novel nucleotide sequenceprotein coding sequences and appropriate transcriptional/translationalcontrol signals. These methods include, for example, in vitrorecombinant DNA techniques, synthetic techniques and in vivorecombination/genetic recombination. See, for example, the techniquesdescribed in Sambrook et al., 1989, supra, and Ausubel et al., 1989,supra. Alternatively, RNA capable of encoding novel nucleotide sequenceprotein sequences may be chemically synthesized using, for example,synthesizers. See, for example, the techniques described in“Oligonucleotide Synthesis”, 1984, Gait, M. J. ed., IRL Press, Oxford,which is incorporated by reference herein in its entirety

A variety of host-expression vector systems may be utilized to expressthe novel nucleotide sequence coding sequences of the invention. Suchhost-expression systems represent vehicles by which the coding sequencesof interest may be produced and subsequently purified, but alsorepresent cells which may, when transformed or transfected with theappropriate nucleotide coding sequences, exhibit the novel proteinencoded by the novel nucleotide sequence of the invention in situ. Theseinclude but are not limited to microorganisms such as bacteria (e.g., E.coli, B. subtilis) transformed with recombinant bacteriophage DNA,plasmid DNA or cosmid DNA expression vectors containing novel nucleotidesequence protein coding sequences; yeast (e.g. Saccharomyces, Pichia)transformed with recombinant yeast expression vectors containing thenovel nucleotide sequence protein coding sequences; insect cell systemsinfected with recombinant virus expression vectors (e.g., baculovirus)containing the novel nucleotide sequence protein coding sequences; plantcell systems infected with recombinant virus expression vectors (e.g.,cauliflower mosaic virus, CaMV; tobacco mosaic virus, TMV) ortransformed with recombinant plasmid expression vectors (e.g., Tiplasmid) containing novel nucleotide sequence protein coding sequences;or mammalian cell systems (e.g. COS, CHO, BHK, 293, 3T3) harboringrecombinant expression constructs containing promoters derived from thegenome of mammalian cells (e.g., metallothionein promoter) or frommammalian viruses (e.g., the adenovirus late promoter; the vacciniavirus 7.5 K promoter).

In bacterial systems, a number of expression vectors may beadvantageously selected depending upon the use intended for the novelnucleotide sequence protein being expressed. For example, when a largequantity of such a protein is to be produced, for the generation ofantibodies or to screen peptide libraries, for example, vectors whichdirect the expression of high levels of fusion protein products that arereadily purified may be desirable. Such vectors include, but are notlimited, to the E. coli expression vector pUR278 (Ruther et al., 1983,EMBO J. 2:1791), in which the novel nucleotide sequence protein codingsequence may be ligated individually into the vector in frame with thelac Z coding region so that a fusion protein is produced; pIN vectors(Inouye & Inouye, 1985, Nucleic Acids Res. 13:3101-3109; Van Heeke &Schuster, 1989, J. Biol. Chem. 264:5503-5509); and the likes of pGEXvectors may also be used to express foreign polypeptides as fusionproteins with glutathione S-transferase (GST). In general, such fusionproteins are soluble and can easily be purified from lysed cells byadsorption to glutathione-agarose beads followed by elution in thepresence of free glutathione. The pGEX vectors are designed to includethrombin or factor Xa protease cleavage sites so that the cloned targetnucleotide sequence protein can be released from the GST moiety. Othersystems useful in the invention include use of the FLAG epitope or the6-HIS systems.

In an insect system, Autographa californica nuclear polyhedrosis virus(AcNPV) is used as a vector to express foreign nucleotide sequences. Thevirus grows in Spodoptera frugiperda cells. The novel nucleotidesequence coding sequence may be cloned individually into non-essentialregions (for example the polyhedrin gene) of the virus and placed undercontrol of an AcNPV promoter (for example the polyhedrin promoter).Successful insertion of novel nucleotide sequence coding sequence willresult in inactivation of the polyhedrin gene and production ofnon-occluded recombinant virus (i.e., virus lacking the proteinaceouscoat coded for by the polyhedrin gene). These recombinant viruses arethen used to infect Spodoptera frugiperda cells in which the insertednucleotide sequence is expressed. (E.g., see Smith et al., 1983, J.Virol. 46: 584; Smith, U.S. Pat. No. 4,215,051).

In mammalian host cells, a number of viral-based expression systems maybe utilized. In cases where an adenovirus is used as an expressionvector, the novel nucleotide sequence coding sequence of interest may beligated to an adenovirus transcription/translation control complex,e.g., the late promoter and tripartite leader sequence. This chimericnucleotide sequence may then be inserted in the adenovirus genome by invitro or in vivo recombination. Insertion in a non-essential region ofthe viral genome (e.g., region E1 or E3) will result in a recombinantvirus that is viable and capable of expressing novel nucleotide sequenceencoded protein in infected hosts. (E.g., See Logan & Shenk, 1984, Proc.Natl. Acad. Sci. USA 81:3655-3659). Specific initiation signals may alsobe required for efficient translation of inserted novel nucleotidesequence coding sequences. These signals include the ATG initiationcodon and adjacent sequences. In cases where an entire novel nucleotidesequence, including its own initiation codon and adjacent sequences, isinserted into the appropriate expression vector, no additionaltranslational control signals may be needed. However, in cases whereonly a portion of the novel nucleotide sequence coding sequence isinserted, exogenous translational control signals, including, perhaps,the ATG initiation codon, must be provided. Furthermore, the initiationcodon must be in phase with the reading frame of the desired codingsequence to ensure translation of the entire insert. These exogenoustranslational control signals and initiation codons can be of a varietyof origins, both natural and synthetic. The efficiency of expression maybe enhanced by the inclusion of appropriate transcription enhancerelements, transcription terminators, etc. (see Bittner et al., 1987,Methods in Enzymol. 153:516-544).

In addition, a host cell strain may be chosen which modulates theexpression of the inserted sequences, or modifies and processes theproduct of the nucleotide sequence in the specific fashion desired. Suchmodifications (e.g., glycosylation) and processing (e.g., cleavage) ofprotein products may be important for the function of the protein.Different host cells have characteristic and specific mechanisms for thepost-translational processing and modification of proteins. Appropriatecell lines or host systems can be chosen to ensure the correctmodification and processing of the foreign protein expressed. To thisend, eukaryotic host cells which possess the cellular machinery forproper processing of the primary transcript, glycosylation, andphosphorylation of the gene product may be used. Such mammalian hostcells include but are not limited to CHO, VERO, BHK, HeLa, COS, MDCK,293, 3T3, WI38, etc.

For long-term, high-yield production of recombinant proteins, stableexpression is preferred. For example, cell lines which stably expressthe novel nucleotide sequence encoded protein may be engineered. Ratherthan using expression vectors which contain viral origins ofreplication, host cells can be transformed with DNA controlled byappropriate expression control elements (e.g., promoter, enhancer,sequences, transcription terminators, polyadenylation sites, etc.), anda selectable marker. Following the introduction of the foreign DNA,engineered cells may be allowed to grow for 1-2 days in an enrichedmedia, and then are switched to a selective media. The selectable markerin the recombinant plasmid confers resistance to the selection andallows cells to stably integrate the plasmid into their chromosomes andgrow to form foci which in turn can be cloned and expanded into celllines. This method may advantageously be used to engineer cell lineswhich express novel nucleotide sequence encoded protein. Such engineeredcell lines may be particularly useful in screening and evaluation ofcompounds that affect the endogenous activity of the novel nucleotidesequence encoded protein.

A number of selection systems may be used, including but not limited tothe herpes simplex virus thymidine kinase (Wigler, et al., 1977, Cell11:223), hypoxanthine-guanine phosphoribosyltransferase (Szybalska &Szybalski, 1962, Proc. Natl. Acad. Sci. USA 48:2026), and adeninephosphoribosyltransferase (Lowy, et al., 1980, Cell 22:817) genes can beemployed in tk-, hgprt- or aprt-cells, respectively. Also,antimetabolite resistance can be used as the basis of selection fordhfr, which confers resistance to methotrexate (Wigler, et al., 1980,Natl. Acad. Sci. USA 77:3567; O'Hare, et al., 1981, Proc. Natl. Acad.Sci. USA 78:1527); gpt, which confers resistance to mycophenolic acid(Mulligan & Berg, 1981, Proc. Natl. Acad. Sci. USA 78:2072); neo, whichconfers resistance to the aminoglycoside G-418 (Colberre-Garapin, etal., 1981, J. Mol. Biol. 150:1); and hygro, which confers resistance tohygromycin (Santerre, et al., 1984, Gene 30:147) genes.

An alternative fusion protein system allows for the ready purificationof non-denatured fusion proteins expressed in human cell lines(Janknecht, et al., 1991, Proc. Natl. Acad. Sci. USA 88: 8972-8976). Inthis system, the nucleotide sequence of interest is subcloned into avaccinia recombination plasmid such that the nucleotide sequence's openreading frame is translationally fused to an amino-terminal tagconsisting of six histidine residues. Extracts from cells infected withrecombinant vaccinia virus are loaded onto Ni.sup.2 +-nitriloaceticacid-agarose columns and histidine-tagged proteins are selectivelyeluted with imidazole-containing buffers.

Where recombinant DNA technology is used to produce the protein encodedby the novel nucleotide sequence for such assay systems, it may beadvantageous to engineer fusion proteins that can facilitate labeling,immobilization and/or detection.

Indirect labeling involves the use of a protein, such as a labeledantibody, which specifically binds to the protein encoded by the novelnucleotide sequence. Such antibodies include but are not limited topolyclonal, monoclonal, chimeric, single chain, Fab fragments andfragments produced by an Fab expression library.

The invention also provides for antibodies to the protein encoded by thenovel nucleotide sequences. Described herein are methods for theproduction of antibodies capable of specifically recognizing one or morenovel nucleotide sequence epitopes. Such antibodies may include, but arenot limited to polyclonal antibodies, monoclonal antibodies (mAbs),humanized or chimeric antibodies, single chain antibodies, Fabfragments, F(ab′)2 fragments, fragments produced by a Fab expressionlibrary, anti-idiotypic (anti-Id) antibodies, and epitope-bindingfragments of any of the above. Such antibodies may be used, for example,in the detection of a novel nucleotide sequence in a biological sample,or, alternatively, as a method for the inhibition of abnormal geneactivity, for example, the inhibition of a disease target nucleotidesequence, as further described below. Thus, such antibodies may beutilized as part of cardiovascular or other disease treatment method,and/or may be used as part of diagnostic techniques whereby patients maybe tested for abnormal levels of novel nucleotide sequence encodedproteins, or for the presence of abnormal forms of the such proteins.

For the production of antibodies to a novel nucleotide sequence, varioushost animals may be immunized by injection with a novel protein encodedby the novel nucleotide sequence, or a portion thereof. Such hostanimals may include but are not limited to rabbits, mice, and rats, toname but a few. Various adjuvants may be used to increase theimmunological response, depending on the host species, including but notlimited to Freund's (complete and incomplete), mineral gels such asaluminum hydroxide, surface active substances such as lysolecithin,pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpethemocyanin, dinitrophenol, and potentially useful human adjuvants suchas BCG (bacille Calmette-Guerin) and Corynebacterium parvum.

Polyclonal antibodies are heterogeneous populations of antibodymolecules derived from the sera of animals immunized with an antigen,such as novel gene product, or an antigenic functional derivativethereof. For the production of polyclonal antibodies, host animals suchas those described above, may be immunized by injection with novel geneproduct supplemented with adjuvants as also described above.

Monoclonal antibodies, which are homogeneous populations of antibodiesto a particular antigen, may be obtained by any technique which providesfor the production of antibody molecules by continuous cell lines inculture. These include, but are not limited to the hybridoma techniqueof Kohler and Milstein, (1975, Nature 256:495-497; and U.S. Pat. No.4,376,110), the human B-cell hybridoma technique (Kosbor et al., 1983,Immunology Today 4:72; Cole et al., 1983, Proc. Natl. Acad. Sci. USA80:2026-2030), and the EBV-hybridoma technique (Cole et al., 1985,Monoclonal Antibodies And Cancer Therapy, Alan R. Liss, Inc., pp.77-96). Such antibodies may be of any immunoglobulin class includingIgG, IgM, IgE, IgA, IgD and any subclass thereof. The hybridomaproducing the mAb of this invention may be cultivated in vitro or invivo.

In addition, techniques developed for the production of “chimericantibodies” (Morrison et al., 1984, Proc. Natl. Acad. Sci.,81:6851-6855; Neuberger et al., 1984, Nature, 312:604-608; Takeda etal., 1985, Nature, 314:452-454) by splicing the genes from a mouseantibody molecule of appropriate antigen specificity together with genesfrom a human antibody molecule of appropriate biological activity can beused. A chimeric antibody is a molecule in which different portions arederived from different animal species, such as those having a variableregion derived from a murine mAb and a human immunoglobulin constantregion.

Alternatively, techniques described for the production of single chainantibodies (U.S. Pat. No. 4,946,778; Bird, 1988, Science 242:423-426;Huston et al., 1988, Proc. Natl. Acad. Sci. USA 85:5879-5883; and Wardet al., 1989, Nature 334:544-546) can be adapted to produce novelnucleotide sequence-single chain antibodies. Single chain antibodies areformed by linking the heavy and light chain fragments of the Fv regionvia an amino acid bridge, resulting in a single chain polypeptide.

Antibody fragments which recognize specific epitopes may be generated byknown techniques For example, such fragments include but are not limitedto: the F(ab′)2 fragments which can be produced by pepsin digestion ofthe antibody molecule and the Fab fragments which can be generated byreducing the disulfide bridges of the F(ab′)2 fragments. Alternatively,Fab expression libraries may be constructed (Huse et al., 1989, Science,246:1275-1281) to allow rapid and easy identification of monoclonal Fabfragments with the desired specificity.

Disease Specific Target Nucleotide Sequences

The invention also provides disease specific target nucleotidesequences, and sets of disease specific target nucleotide sequences. Thediagnostic nucleotide sets, subsets thereof, novel nucleotide sequences,and individual members of the diagnostic nucleotide sets identified asdescribed above are also disease specific target nucleotide sequences.In particular, individual nucleotide sequences that are differentiallyregulated or have predictive value that is strongly correlated with adisease or disease criterion are especially favorable as diseasespecific target nucleotide sequences. Sets of genes that areco-regulated may also be identified as disease specific targetnucleotide sets. Such nucleotide sequences and/or nucleotide sequenceproducts are targets for modulation by a variety of agents andtechniques. For example, disease specific target nucleotide sequences(or the products of such nucleotide sequences, or sets of diseasespecific target nucleotide sequences) can be inhibited or activated by,e.g., target specific monoclonal antibodies or small moleculeinhibitors, or delivery of the nucleotide sequence or gene product ofthe nucleotide sequence to patients. Also, sets of genes can beinhibited or activated by a variety of agents and techniques. Thespecific usefulness of the target nucleotide sequence(s) depends on thesubject groups from which they were discovered, and the disease ordisease criterion with which they correlate.

Imaging

The invention also provides for imaging reagents. The differentiallyexpressed leukocyte nucleotide sequences, diagnostic nucleotide sets, orportions thereof, and novel nucleotide sequences of the invention arenucleotide sequences expressed in cells with or without disease.Leukocytes expressing a nucleotide sequence(s) that is differentiallyexpressed in a disease condition may localize within the body to sitesthat are of interest for imaging purposes. For example, a leukocyteexpressing a nucleotide sequence(s) that are differentially expressed inan individual having atherosclerosis may localize or accumulate at thesite of an atherosclerotic placque. Such leukocytes, when labeled, mayprovide a detection reagent for use in imaging regions of the body wherelabeled leukocyte accumulate or localize, for example, at theatherosclerotic plaque in the case of atherosclerosis. For example,leukocytes are collected from a subject, labeled in vitro, andreintroduced into a subject. Alternatively, the labeled reagent isintroduced into the subject individual, and leukocyte labeling occurswithin the patient.

Imaging agents that detect the imaging targets of the invention areproduced by well-known molecular and immunological methods (forexemplary protocols, see, e.g., Ausubel, Berger, and Sambrook, as wellas Harlow and Lane, supra).

For example, a full-length nucleic acid sequence, or alternatively, agene fragment encoding an immunogenic peptide or polypeptide fragments,is cloned into a convenient expression vector, for example, a vectorincluding an in-frame epitope or substrate binding tag to facilitatesubsequent purification. Protein is then expressed from the cloned cDNAsequence and used to generate antibodies, or other specific bindingmolecules, to one or more antigens of the imaging target protein.Alternatively, a natural or synthetic polypeptide (or peptide) or smallmolecule that specifically binds (or is specifically bound to) theexpressed imaging target can be identified through well establishedtechniques (see, e.g., Mendel et al. (2000) Anticancer Drug Des15:29-41; Wilson (2000) Curr Med Chem 7:73-98; Hamby and Showwalter(1999) Pharmacol Ther 82:169-93; and Shimazawa et al. (1998) Curr OpinStruct Biol 8:451-8). The binding molecule, e.g., antibody, smallmolecule ligand, etc., is labeled with a contrast agent or otherdetectable label, e.g., gadolinium, iodine, or a gamma-emitting source.For in-vivo imaging of a disease process that involved leukocytes, thelabeled antibody is infused into a subject, e.g., a human patient oranimal subject, and a sufficient period of time is passed to permitbinding of the antibody to target cells. The subject is then imaged withappropriate technology such as MRI (when the label is gadolinium) orwith a gamma counter (when the label is a gamma emitter).

Identification of Nucleotide Sequence Involved in Leukocyte Adhesion

The invention also encompasses a method of identifying nucleotidesequences involved in leukocyte adhesion. The interaction between theendothelial cell and leukocyte is a fundamental mechanism of allinflammatory disorders, including the diseases listed in Table 1. Forexample, the first visible abnormality in atherosclerosis is theadhesion to the endothelium and diapedesis of mononuclear cells (e.g.,T-cell and monocyte). Insults to the endothelium (for example,cytokines, tobacco, diabetes, hypertension and many more) lead toendothelial cell activation. The endothelium then expresses adhesionmolecules, which have counter receptors on mononuclear cells. Once theleukocyte receptors have bound the endothelial adhesion molecules, theystick to the endothelium, roll a short distance, stop and transmigrateacross the endothelium. A similar set of events occurs in both acute andchronic inflammation. When the leukocyte binds the endothelial adhesionmolecule, or to soluble cytokines secreted by endothelial or othercells, a program of gene expression is activated in the leukocyte. Thisprogram of expression leads to leukocyte rolling, firm adhesion andtransmigration into the vessel wall or tissue parenchyma. Inhibition ofthis process is highly desirable goal in anti-inflammatory drugdevelopment. In addition, leukocyte nucleotide sequences and epithelialcell nucleotide sequences, that are differentially expressed during thisprocess may be disease-specific target nucleotide sequences.

Human endothelial cells, e.g. derived from human coronary arteries,human aorta, human pulmonary artery, human umbilical vein ormicrovascular endothelial cells, are cultured as a confluent monolayer,using standard methods. Some of the endothelial cells are then exposedto cytokines or another activating stimuli such as oxidized LDL,hyperglycemia, shear stress, or hypoxia (Moser et al. 1992). Someendothelial cells are not exposed to such stimuli and serve as controls.For example, the endothelial cell monolayer is incubated with culturemedium containing 5 U/ml of human recombinant IL-1alpha or 10 ng/ml TNF(tumor necrosis factor), for a period of minutes to overnight. Theculture medium composition is changed or the flask is sealed to inducehypoxia. In addition, tissue culture plate is rotated to induce sheerstress.

Human T-cells and/or monocytes are cultured in tissue culture flasks orplates, with LGM-3 media from Clonetics. Cells are incubated at 37degree C., 5% CO2 and 95% humidity. These leukocytes are exposed to theactivated or control endothelial layer by adding a suspension ofleukocytes on to the endothelial cell monolayer. The endothelial cellmonolayer is cultured on a tissue culture treated plate/flask or on amicroporous membrane. After a variable duration of exposures, theendothelial cells and leukocytes are harvested separately by treatingall cells with trypsin and then sorting the endothelial cells from theleukocytes by magnetic affinity reagents to an endothelial cell specificmarker such as PECAM-1 (Stem Cell Technologies). RNA is extracted fromthe isolated cells by standard techniques. Leukocyte RNA is labeled asdescribed above, and hybridized to leukocyte candidate nucleotidelibrary. Epithelial cell RNA is also labeled and hybridized to theleukocyte candidate nucleotide library. Alternatively, the epithelialcell RNA is hybridized to a epithelial cell candidate nucleotidelibrary, prepared according to the methods described for leukocytecandidate libraries, above.

Hybridization to candidate nucleotide libraries will reveal nucleotidesequences that are up-regulated or down-regulated in leukocyte and/orepithelial cells undergoing adhesion. The differentially regulatednucleotide sequences are further characterized, e.g. by isolating andsequencing the full-length sequence, analysis of the DNA and predictedprotein sequence, and functional characterization of the protein productof the nucleotide sequence, as described above. Further characterizationmay result in the identification of leukocyte adhesion specific targetnucleotide sequences, which may be candidate targets for regulation ofthe inflammatory process. Small molecule or antibody inhibitors can bedeveloped to inhibit the target nucleotide sequence function. Suchinhibitors are tested for their ability to inhibit leukocyte adhesion inthe in vitro test described above.

Integrated Systems

Integrated systems for the collection and analysis of expressionprofiles, and molecular signatures, as well as for the compilation,storage and access of the databases of the invention, typically includea digital computer with software including an instruction set forsequence searching and analysis, and, optionally, high-throughput liquidcontrol software, image analysis software, data interpretation software,a robotic control armature for transferring solutions from a source to adestination (such as a detection device) operably linked to the digitalcomputer, an input device (e.g., a computer keyboard) for enteringsubject data to the digital computer, or to control analysis operationsor high throughput sample transfer by the robotic control armature.Optionally, the integrated system further comprises an image scanner fordigitizing label signals from labeled assay components, e.g., labelednucleic acid hybridized to a candidate library microarray. The imagescanner can interface with image analysis software to provide ameasurement of the presence or intensity of the hybridized label, i.e.,indicative of an on/off expression pattern or an increase or decrease inexpression.

Readily available computational hardware resources using standardoperating systems are fully adequate, e.g., a PC (Intel x86 or Pentiumchip-compatible DOS,™ OS2,™ M WINDOWS,™ M WINDOWS NT,™ WINDOWS95,™WINDOWS98,™ LINUX, or even Macintosh, Sun or PCs will suffice) for usein the integrated systems of the invention. Current art in softwaretechnology is similarly adequate (i.e., there are a multitude of matureprogramming languages and source code suppliers) for design, e.g., of anupgradeable open-architecture object-oriented heuristic algorithm, orinstruction set for expression analysis, as described herein. Forexample, software for aligning or otherwise manipulating, molecularsignatures can be constructed by one of skill using a standardprogramming language such as Visual basic, Fortran, Basic, Java, or thelike, according to the methods herein.

Various methods and algorithms, including genetic algorithms and neuralnetworks, can be used to perform the data collection, correlation, andstorage functions, as well as other desirable functions, as describedherein. In addition, digital or analog systems such as digital or analogcomputer systems can control a variety of other functions such as thedisplay and/or control of input and output files.

For example, standard desktop applications such as word processingsoftware (e.g., Corel WordPerfect™ or Microsoft Word™) and databasesoftware (e.g., spreadsheet software such as Corel Quattro Pro™,Microsoft Excel™, or database programs such as Microsoft Access™ orParadox™) can be adapted to the present invention by inputting one ormore character string corresponding, e.g., to an expression pattern orprofile, subject medical or historical data, molecular signature, or thelike, into the software which is loaded into the memory of a digitalsystem, and carrying out the operations indicated in an instruction set,e.g., as exemplified in FIG. 2. For example, systems can include theforegoing software having the appropriate character string information,e.g., used in conjunction with a user interface in conjunction with astandard operating system such as a Windows, Macintosh or LINUX system.For example, an instruction set for manipulating strings of characters,either by programming the required operations into the applications orwith the required operations performed manually by a user (or both). Forexample, specialized sequence alignment programs such as PILEUP or BLASTcan also be incorporated into the systems of the invention, e.g., foralignment of nucleic acids or proteins (or corresponding characterstrings).

Software for performing the statistical methods required for theinvention, e.g., to determine correlations between expression profilesand subsets of members of the diagnostic nucleotide libraries, such asprogrammed embodiments of the statistical methods described above, arealso included in the computer systems of the invention. Alternatively,programming elements for performing such methods as principle componentanalysis (PCA) or least squares analysis can also be included in thedigital system to identify relationships between data. Exemplarysoftware for such methods is provided by Partek, Inc., St. Peter, Mo.;at the web site partek.com.

Any controller or computer optionally includes a monitor which caninclude, e.g., a flat panel display (e.g., active matrix liquid crystaldisplay, liquid crystal display), a cathode ray tube (“CRT”) display, oranother display system which serves as a user interface, e.g., to outputpredictive data. Computer circuitry, including numerous integratedcircuit chips, such as a microprocessor, memory, interface circuits, andthe like, is often placed in a casing or box which optionally alsoincludes a hard disk drive, a floppy disk drive, a high capacityremovable drive such as a writeable CD-ROM, and other common peripheralelements.

Inputting devices such as a keyboard, mouse, or touch sensitive screen,optionally provide for input from a user and for user selection, e.g.,of sequences or data sets to be compared or otherwise manipulated in therelevant computer system. The computer typically includes appropriatesoftware for receiving user instructions, either in the form of userinput into a set parameter or data fields (e.g., to input relevantsubject data), or in the form of preprogrammed instructions, e.g.,preprogrammed for a variety of different specific operations. Thesoftware then converts these instructions to appropriate language forinstructing the system to carry out any desired operation.

The integrated system may also be embodied within the circuitry of anapplication specific integrated circuit (ASIC) or programmable logicdevice (PLD). In such a case, the invention is embodied in a computerreadable descriptor language that can be used to create an ASIC or PLD.The integrated system can also be embodied within the circuitry or logicprocessors of a variety of other digital apparatus, such as PDAs, laptopcomputer systems, displays, image editing equipment, etc.

The digital system can comprise a learning component where expressionprofiles, and relevant subject data are compiled and monitored inconjunction with physical assays, and where correlations, e.g.,molecular signatures with predictive value for a disease, areestablished or refined. Successful and unsuccessful combinations areoptionally documented in a database to provide justification/preferencesfor user-base or digital system based selection of diagnostic nucleotidesets with high predictive accuracy for a specified disease or condition.

The integrated systems can also include an automated workstation. Forexample, such a workstation can prepare and analyze leukocyte RNAsamples by performing a sequence of events including: preparing RNA froma human blood sample; labeling the RNA with an isotopic or non-isotopiclabel; hybridizing the labeled RNA to at least one array comprising allor part of the candidate library; and detecting the hybridizationpattern. The hybridization pattern is digitized and recorded in theappropriate database.

Automated RNA Preparation Tool

The invention also includes an automated RNA preparation tool for thepreparation of mononuclear cells from whole blood samples, andpreparation of RNA from the mononuclear cells. In a preferredembodiment, the use of the RNA preparation tool is fully automated, sothat the cell separation and RNA isolation would require no humanmanipulations. Full automation is advantageous because it minimizesdelay, and standardizes sample preparation across differentlaboratories. This standardization increases the reproducibility of theresults.

FIG. 2 depicts the processes performed by the RNA preparation tool ofthe invention. A primary component of the device is a centrifuge (A).Tubes of whole blood containing a density gradient solution,transcription/translation inhibitors, and a gel barrier that separateserythrocytes from mononuclear cells and serum after centrifugation areplaced in the centrifuge (B). The barrier is permeable to erythrocytesand granulocytes during centrifugation, but does not allow mononuclearcells to pass through (or the barrier substance has a density such thatmononuclear cells remain above the level of the barrier during thecentrifugation). After centrifugation, the erythrocytes and granulocytesare trapped beneath the barrier, facilitating isolation of themononuclear cell and serum layers. A mechanical arm removes the tube andinverts it to mix the mononuclear cell layer and the serum (C). The armnext pours the supernatant into a fresh tube (D), while the erythrocytesand granulocytes remained below the barrier. Alternatively, a needle isused to aspirate the supernatant and transfer it to a fresh tube. Themechanical arms of the device opens and closes lids, dispenses PBS toaid in the collection of the mononuclear cells by centrifugation, andmoves the tubes in and out of the centrifuge. Following centrifugation,the supernatant is poured off or removed by a vacuum device (E), leavingan isolated mononuclear cell pellet. Purification of the RNA from thecells is performed automatically, with lysis buffer and otherpurification solutions (F) automatically dispensed and removed beforeand after centrifugation steps. The result is a purified RNA solution.In another embodiment, RNA isolation is performed using a column orfilter method. In yet another embodiment, the invention includes anon-board homogenizer for use in cell lysis.

Other Automated Systems

Automated and/or semi-automated methods for solid and liquid phasehigh-throughput sample preparation and evaluation are available, andsupported by commercially available devices. For example, roboticdevices for preparation of nucleic acids from bacterial colonies, e.g.,to facilitate production and characterization of the candidate libraryinclude, for example, an automated colony picker (e.g., the Q-bot,Genetix, U.K.) capable of identifying, sampling, and inoculating up to10,000/4 hrs different clones into 96 well microtiter dishes.Alternatively, or in addition, robotic systems for liquid handling areavailable from a variety of sources, e.g., automated workstations likethe automated synthesis apparatus developed by Takeda ChemicalIndustries, LTD. (Osaka, Japan) and many robotic systems utilizingrobotic arms (Zymate II, Zymark Corporation, Hopkinton, Mass.; Orca,Beckman Coulter, Inc. (Fullerton, Calif.)) which mimic the manualoperations performed by a scientist. Any of the above devices aresuitable for use with the present invention, e.g., for high-throughputanalysis of library components or subject leukocyte samples. The natureand implementation of modifications to these devices (if any) so thatthey can operate as discussed herein will be apparent to persons skilledin the relevant art.

High throughput screening systems that automate entire procedures, e.g.,sample and reagent pipetting, liquid dispensing, timed incubations, andfinal readings of the microplate in detector(s) appropriate for therelevant assay are commercially available. (see, e.g., Zymark Corp.,Hopkinton, Mass.; Air Technical Industries, Mentor, Ohio; BeckmanInstruments, Inc. Fullerton, Calif.; Precision Systems, Inc., Natick,Mass., etc.). These configurable systems provide high throughput andrapid start up as well as a high degree of flexibility andcustomization. Similarly, arrays and array readers are available, e.g.,from Affymetrix, PE Biosystems, and others.

The manufacturers of such systems provide detailed protocols the varioushigh throughput. Thus, for example, Zymark Corp. provides technicalbulletins describing screening systems for detecting the modulation ofgene transcription, ligand binding, and the like.

A variety of commercially available peripheral equipment, including,e.g., optical and fluorescent detectors, optical and fluorescentmicroscopes, plate readers, CCD arrays, phosphorimagers, scintillationcounters, phototubes, photodiodes, and the like, and software isavailable for digitizing, storing and analyzing a digitized video ordigitized optical or other assay results, e.g., using PC (Intel x86 orpentium chip-compatible DOS™, OS2™ WINDOWS™, WINDOWS NT™ or WINDOWS95™based machines), MACINTOSH™, or UNIX based (e.g., SUN™ work station)computers.

Embodiment in a Web Site.

The methods described above can be implemented in a localized ordistributed computing environment. For example, if a localized computingenvironment is used, an array comprising a candidate nucleotide library,or diagnostic nucleotide set, is configured in proximity to a detector,which is, in turn, linked to a computational device equipped with userinput and output features.

In a distributed environment, the methods can be implemented on a singlecomputer with multiple processors or, alternatively, on multiplecomputers. The computers can be linked, e.g. through a shared bus, butmore commonly, the computer(s) are nodes on a network. The network canbe generalized or dedicated, at a local level or distributed over a widegeographic area. In certain embodiments, the computers are components ofan intra-net or an internet.

The predictive data corresponding to subject molecular signatures (e.g.,expression profiles, and related diagnostic, prognostic, or monitoringresults) can be shared by a variety of parties. In particular, suchinformation can be utilized by the subject, the subject's health carepractitioner or provider, a company or other institution, or ascientist. An individual subject's data, a subset of the database or theentire database recorded in a computer readable medium can be accesseddirectly by a user by any method of communication, including, but notlimited to, the internet. With appropriate computational devices,integrated systems, communications networks, users at remote locations,as well as users located in proximity to, e.g., at the same physicalfacility, the database can access the recorded information. Optionally,access to the database can be controlled using unique alphanumericpasswords that provide access to a subset of the data. Such provisionscan be used, e.g., to ensure privacy, anonymity, etc.

Typically, a client (e.g., a patient, practitioner, provider, scientist,or the like) executes a Web browser and is linked to a server computerexecuting a Web server. The Web browser is, for example, a program suchas IBM's Web Explorer, Internet explorer, NetScape or Mosaic, or thelike. The Web server is typically, but not necessarily, a program suchas IBM's HTTP Daemon or other WWW daemon (e.g., LINUX-based forms of theprogram). The client computer is bi-directionally coupled with theserver computer over a line or via a wireless system. In turn, theserver computer is bi-directionally coupled with a website (serverhosting the website) providing access to software implementing themethods of this invention.

A user of a client connected to the Intranet or Internet may cause theclient to request resources that are part of the web site(s) hosting theapplication(s) providing an implementation of the methods describedherein. Server program(s) then process the request to return thespecified resources (assuming they are currently available). A standardnaming convention has been adopted, known as a Uniform Resource Locator(“URL”). This convention encompasses several types of location names,presently including subclasses such as Hypertext Transport Protocol(“http”), File Transport Protocol (“ftp”), gopher, and Wide AreaInformation Service (“WAIS”). When a resource is downloaded, it mayinclude the URLs of additional resources. Thus, the user of the clientcan easily learn of the existence of new resources that he or she hadnot specifically requested.

Methods of implementing Intranet and/or Intranet embodiments ofcomputational and/or data access processes are well known to those ofskill in the art and are documented, e.g., in ACM Press, pp. 383-392;ISO-ANSI, Working Draft, “Information Technology-Database Language SQL”,Jim Melton, Editor, International Organization for Standardization andAmerican National Standards Institute, July 1992; ISO Working Draft,“Database Language SQL-Part 2:Foundation (SQL/Foundation)”,CD9075-2:199.chi.SQL, Sep. 11, 1997; and Cluer et al. (1992) A GeneralFramework for the Optimization of Object-Oriented Queries, Proc SIGMODInternational Conference on Management of Data, San Diego, Calif., Jun.2-5, 1992, SIGMOD Record, vol. 21, Issue 2, June, 1992; Stonebraker, M.,Editor. Other resources are available, e.g., from Microsoft, IBM, Sunand other software development companies.

Using the tools described above, users of the reagents, methods anddatabase as discovery or diagnostic tools can query a centrally locateddatabase with expression and subject data. Each submission of data addsto the sum of expression and subject information in the database. Asdata is added, a new correlation statistical analysis is automaticallyrun that incorporates the added clinical and expression data.Accordingly, the predictive accuracy and the types of correlations ofthe recorded molecular signatures increases as the database grows.

For example, subjects, such as patients, can access the results of theexpression analysis of their leukocyte samples and any accrued knowledgeregarding the likelihood of the patient's belonging to any specifieddiagnostic (or prognostic, or monitoring, or risk group), i.e., theirexpression profiles, and/or molecular signatures. Optionally, subjectscan add to the predictive accuracy of the database by providingadditional information to the database regarding diagnoses, testresults, clinical or other related events that have occurred since thetime of the expression profiling. Such information can be provided tothe database via any form of communication, including, but not limitedto, the internet. Such data can be used to continually define (andredefine) diagnostic groups. For example, if 1000 patients submit dataregarding the occurrence of myocardial infarction over the 5 years sincetheir expression profiling, and 300 of these patients report that theyhave experienced a myocardial infarction and 700 report that they havenot, then the 300 patients define a new “group A.” As the algorithm isused to continually query and revise the database, a new diagnosticnucleotide set that differentiates groups A and B (i.e., with andwithout myocardial infarction within a five year period) is identified.This newly defined nucleotide set is then be used (in the mannerdescribed above) as a test that predicts the occurrence of myocardialinfarction over a five-year period. While submission directly by thepatient is exemplified above, any individual with access and authorityto submit the relevant data e.g., the patient's physician, a laboratorytechnician, a health care or study administrator, or the like, can doso.

As will be apparent from the above examples, transmission of informationvia the internet (or via an intranet) is optionally bi-directional. Thatis, for example, data regarding expression profiles, subject data, andthe like are transmitted via a communication system to the database,while information regarding molecular signatures, predictive analysis,and the like, are transmitted from the database to the user. Forexample, using appropriate configurations of an integrated systemincluding a microarray comprising a diagnostic nucleotide set, adetector linked to a computational device can directly transmit (locallyor from a remote workstation at great distance, e.g., hundreds orthousands of miles distant from the database) expression profiles and acorresponding individual identifier to a central database for analysisaccording to the methods of the invention. According to, e.g., thealgorithms described above, the individual identifier is assigned to oneor more diagnostic (or prognostic, or monitoring, etc.) categories. Theresults of this classification are then relayed back, via, e.g., thesame mode of communication, to a recipient at the same or differentinternet (or intranet) address.

Kits

The present invention is optionally provided to a user as a kit.Typically, a kit contains one or more diagnostic nucleotide sets of theinvention. Alternatively, the kit contains the candidate nucleotidelibrary of the invention. Most often, the kit contains a diagnosticnucleotide probe set, or other subset of a candidate library, e.g., as acDNA or antibody microarray packaged in a suitable container. The kitmay further comprise, one or more additional reagents, e.g., substrates,labels, primers, for labeling expression products, tubes and/or otheraccessories, reagents for collecting blood samples, buffers, e.g.,erythrocyte lysis buffer, leukocyte lysis buffer, hybridizationchambers, cover slips, etc., as well as a software package, e.g.,including the statistical methods of the invention, e.g., as describedabove, and a password and/or account number for accessing the compileddatabase. The kit optionally further comprises an instruction set oruser manual detailing preferred methods of using the diagnosticnucleotide sets in the methods of the invention. Exemplary kits aredescribed in FIG. 3.

This invention will be better understood by reference to the followingnon-limiting Examples:

EXAMPLES List of Example Titles

Example 1 Generation of subtracted leukocyte candidate nucleotidelibraryExample 2 Identification of nucleotide sequences for candidate libraryusing data mining techniquesExample 3 DNA Sequencing and Processing of raw sequence data.Example 4 Further sequence analysis of novel nucleotide sequencesidentified by subtractive hybridization screeningExample 5 Further sequence analysis of novel Clone 596H6Example 6 Further sequence analysis of novel Clone 486E11Example 7 Preparation of a Leukocyte cDNA Array Comprising a CandidateGene LibraryExample 8 Preparation of RNA from Mononuclear Cells for ExpressionProfilingExample 9 Preparation of Universal Control RNA for Use in LeukocyteExpression profilingExample 10 RNA Labeling and hybridization to a leukocyte cDNA array ofcandidate nucleotide sequences.Example 11 Clinical study for the Identification of diagnostic gene setsuseful in diagnosis and treatment of Cardiac allograft rejectionExample 12 Identification of diagnostic nucleotide sets for kidney andliver allograft rejectionExample 13 Identification of diagnostic nucleotide sequences sets foruse in the diagnosis and treatment of Atherosclerosis, Stable AnginaPectoris, and acute coronary syndrome.Example 14 Identification of diagnostic nucleotide sets for use indiagnosing and treating RestenosisExample 15 Identification of diagnostic nucleotide sets for use inmonitoring treatment and/or progression of Congestive Heart FailureExample 16 Identification of diagnostic nucleotide sets for use indiagnosis of rheumatoid arthritis.Example 17 Identification of diagnostic nucleotide sets for diagnosis ofcytomegalovirusExample 18 Identification of diagnostic nucleotide sets for diagnosis ofEpstein Barr VirusExample 19 Identification of diagnostic nucleotides sets for monitoringresponse to statin drugs.Example 20 Probe selection for a 24,000 feature Array.Example 21 Design of oligonucleotide probes.Example 22 Production of an array of 8,000 spotted 50 meroligonucleotides.Example 23 Amplification, labeling and hybridization of total RNA to anoligonucleotide microarray.Example 24 Analysis of Human Transplant Patient Mononuclear cell RNAHybridized to a 24,000 Feature Microarray.Example 25 Real-time PCR validation of array expression results

Example 26 Correlation and Classification Analysis EXAMPLES Example 1Generation of Subtracted Leukocyte Candidate Nucleotide Library

To produce a candidate nucleotide library with representatives from thespectrum of nucleotide sequences that are differentially expressed inleukocytes, subtracted hybridization libraries were produced from thefollowing cell types and conditions:

1. Buffy Coat leukocyte fractions—stimulated with ionomycin and PMA

2. Buffy Coat leukocyte fractions—un-stimulated

3. Peripheral blood mononuclear cells—stimulated with ionomycin and PMA

4. Peripheral blood mononuclear cells—un-stimulated

5. T lymphocytes—stimulated with PMA and ionomycin

6. T lymphocytes—resting

Cells were obtained from multiple individuals to avoid introduction ofbias by using only one person as a cell source.

Buffy coats (platelets and leukocytes that are isolated from wholeblood) were purchased from Stanford Medical School Blood Center. Fourbuffy coats were used, each of which was derived from about 350 ml ofwhole blood from one donor individual 10 ml of buffy coat sample wasdrawn from the sample bag using a needle and syringe. 40 ml of Buffer EL(Qiagen) was added per 10 ml of buffy coat to lyse red blood cells. Thesample was placed on ice for 15 minutes, and cells were collected bycentrifugation at 2000 rpm for 10 minutes. The supernatant was decantedand the cell pellet was re-suspended in leukocyte growth mediasupplemented with DNase (LGM-3 from Clonetics supplemented with Dnase ata final concentration of 30 U/ml). Cell density was determined using ahemocytometer. Cells were plated in media at a density of 1×10⁶ cells/mlin a total volume of 30 ml in a T-75 flask (Corning). Half of the cellswere stimulated with ionomycin and phorbol myristate acetate (PMA) at afinal concentration of 1 μg/ml and 62 ng/ml, respectively. Cells wereincubated at 37° C. and at 5% CO₂ for 3 hours, then cells were scrapedoff the flask and collected into 50 ml tubes. Stimulated and restingcell populations were kept separate. Cells were centrifuged at 2000 rpmfor 10 minutes and the supernatant was removed. Cells were lysed in 6 mlof phenol/guanidine isothyocyanate (Trizol reagent, GibcoBRL),homogenized using a rotary homogenizer, and frozen at 80°. Total RNA andmRNA were isolated as described below.

Two frozen vials of 5×10⁶ human peripheral blood mononuclear cells(PBMCs) were purchased from Clonetics (catalog number α-2702). The cellswere rapidly thawed in a 37° C. water bath and transferred to a 15 mltube containing 10 ml of leukocyte growth media supplemented with DNase(prepared as described above). Cells were centrifuged at 200 μg for 10minutes. The supernatant was removed and the cell pellet was resuspendedin LGM-3 media supplemented with DNase. Cell density was determinedusing a hemocytometer. Cells were plated at a density of 1×10⁶ cells/mlin a total volume of 30 ml in a T-75 flask (Corning). Half of the cellswere stimulated with ionomycin and PMA at a final concentration of 1μg/ml and 62 ng/ml, respectively. Cells were incubated at 37° C. and at5% CO₂ for 3 hours, then cells were scraped off the flask and collectedinto 50 ml tubes. Stimulated and resting cell populations were keptseparate. Cells were centrifuged at 2000 rpm and the supernatant wasremoved. Cells were lysed in 6 ml of phenol/guanidine isothyocyanatesolution (TRIZOL reagent, GibcoBRL)), homogenized using a rotaryhomogenizer, and frozen at 80°. Total RNA and mRNA were isolated fromthese samples using the protocol described below.

45 ml of whole blood was drawn from a peripheral vein of four healthyhuman subjects into tubes containing anticoagulant. 50 μl RosetteSep(Stem Cell Technologies) cocktail per ml of blood was added, mixed well,and incubated for 20 minutes at room temperature. The mixture wasdiluted with an equal volume of PBS+2% fetal bovine serum (FBS) andmixed by inversion. 30 ml of diluted mixture sample was layered on topof 15 ml DML medium (Stem Cell Technologies). The sample tube wascentrifuged for 20 minutes at 1200×g at room temperature. The enrichedT-lymphocyte cell layer at the plasma: medium interface was removed.Enriched cells were washed with PBS+2% FBS and centrifuged at 1200×g.The cell pellet was treated with 5 ml of erythrocyte lysis buffer (ELbuffer, Qiagen) for 10 minutes on ice. The sample was centrifuged for 5min at 1200 g. Cells were plated at a density of 1×10⁶ cells/ml in atotal volume of 30 ml in a T-75 flask (Corning). Half of the cells werestimulated with ionomycin and PMA at a final concentration of 1 μg/mland 62 ng/ml, respectively. Cells were incubated at 37° C. and at 5% CO₂for 3 hours, then cells were scraped off the flask and collected into 50ml tubes. Stimulated and resting cell populations were kept separate.Cells were centrifuged at 2000 rpm and the supernatant was removed.Cells were lysed in 6 ml of phenol/guanidine isothyocyanate solution(TRIZOL reagent, GibcoBRL), homogenized using a rotary homogenizer, andfrozen at 80°. Total RNA and mRNA were isolated as described below.

Total RNA and mRNA were isolated using the following procedure: thehomogenized samples were thawed and mixed by vortexing. Samples werelysed in a 1:0.2 mixture of Trizol and chloroform, respectively. Forsome samples, 6 ml of Trizol-chloroform was added. Variable amounts ofTrizol-chloroform was added to other samples. Following lysis, sampleswere centrifuged at 3000 g for 15 min at 4° C. The aqueous layer wasremoved into a clean tube and 4 volumes of Buffer RLT Qiagen) was addedfor every volume of aqueous layer. The samples were mixed thoroughly andtotal RNA was prepared from the sample by following the Qiagen Rneasymidi protocol for RNA cleanup (October 1999 protocol, Qiagen). For thefinal step, the RNA was eluted from the column twice with 250 μlRnase-free water. Total RNA was quantified using a spectrophotometer.Isolation of mRNA from total RNA sample was done using The Oligotex mRNAisolation protocol (Qiagen) was used to isolate mRNA from total RNA,according to the manufacturer's instructions (Qiagen, 7/99 version).mRNA was quantified by spectrophotometry.

Subtracted cDNA libraries were prepared using Clontech's PCR-Select cDNASubtraction Kit (protocol number PT-1117-1) as described in themanufacturer's protocol. The protocol calls for two sources of RNA perlibrary, designated “Driver” and “Tester.” The following 6 librarieswere made:

Library Driver RNA Tester RNA Buffy Coat Stimulated Un-stimulated BuffyCoat Stimulated Buffy Coat Buffy Coat Resting Stimulated Buffy CoatUn-stimulated Buffy Coat PBMC Stimulated Un-stimulated PBMCs StimulatedPBMCs PBMC Resting Stimulated PBMCs Un-stimulated PBMCs T-cellStimulated Un-stimulated T-cells Stimulated T-cells T-cell RestingStimulated T-cells Un-stimulated T-cells

The Clontech protocol results in the PCR amplification of cDNA products.The PCR products of the subtraction protocol were ligated to the pGEMT-easy bacterial vector as described by the vector manufacturer (Promega6/99 version). Ligated vector was transformed into competent bacteriausing well-known techniques, plated, and individual clones are picked,grown and stored as a glycerol stock at −80 C. Plasmid DNA was isolatedfrom these bacteria by standard techniques and used for sequenceanalysis of the insert. Unique cDNA sequences were searched in theUnigene database (build 133), and Unigene cluster numbers wereidentified that corresponded to the DNA sequence of the cDNA. Unigenecluster numbers were recorded in an Excel spreadsheet.

Example 2 Identification of Nucleotide Sequences for Candidate LibraryUsing Data Mining Techniques

Existing and publicly available gene sequence databases were used toidentify candidate nucleotide sequences for leukocyte expressionprofiling. Genes and nucleotide sequences with specific expression inleukocytes, for example, lineage specific markers, or known differentialexpression in resting or activated leukocytes were identified. Suchnucleotide sequences are used in a leukocyte candidate nucleotidelibrary, alone or in combination with nucleotide sequences isolatedthrough cDNA library construction, as described above.

Leukocyte candidate nucleotide sequences were identified using threeprimary methods. First, the publically accessible publication databasePubMed was searched to identify nucleotide sequences with known specificor differential expression in leukocytes. Nucleotide sequences wereidentified that have been demonstrated to have differential expressionin peripheral blood leukocytes between subjects with and withoutparticular disease(s) selected from Table 1. Additionally, genes andgene sequences that were known to be specific or selective forleukocytes or sub-populations of leukocytes were identified in this way.

Next, two publicly available databases of DNA sequences, Unigenea at theweb site ncbi.nlm.nih.gov/UniGene/) and BodyMap(bodymap.ims.u-tokyo.ac.jp/), were searched for sequenced DNA clonesthat showed specificity to leukocyte lineages, or subsets of leukocytes,or resting or activated leukocytes.

The human Unigene database (build 133) was used to identify leukocytecandidate nucleotide sequences that were likely to be highly orexclusively expressed in leukocytes. We used the Library DifferentialDisplay utility of Unigene, which uses statistical methods (The FisherExact Test) to identify nucleotide sequences that have relativespecificity for a chosen library or group of libraries relative to eachother. We compared the following human libraries from Unigene release133:

546 NCI_CGAP_HSC1 (399)

848 Human_mRNA_from_cd34+_stem_cells (122)

105 CD34+DIRECTIONAL (150)

3587 KRIBB_Human_CD4_intrathymic_T-cell_cDNA_library (134)

3586 KRIBB_Human_DP_intrathymic_T-cell_cDNA_library (179)

3585 KRIBB_Human_TN_intrathymic_T-cell_cDNA_library (127)

3586 323 Activated_T-cells_I (740)

376 Activated_T-cells_XX (1727)

327 Monocytes,_stimulated_II (110)

824 Proliferating_Erythroid_Cells_(LCB:ad_library) (665)

825 429 Macrophage_II (105)

387 Macrophage_I (137)

669 NCI_CGAP_CLL1 (11626)

129 Human_White_blood_cells (922)

1400 NIH_MGC_(—)2 (422)

55 Human_promyelocyte (1220)

1010 NCI_CGAP_CML1 (2541)

2217 NCI_CGAP_Sub7 (218)

1395 NCI_CGAP_Sub6 (2764)

4874 NIH_MGC_(—)48 (2524)

BodyMap, like Unigene, contains cell-specific libraries that containpotentially useful information about genes that may serve aslineage-specific or leukocyte specific markers (Okubo et al. 1992). Wecompared three leukocyte specific libraries, Granulocyte, CD4 T cell,and CD8 T cell, with the other libraries. Nucleotide sequences that werefound in one or more of the leukocyte-specific libraries, but absent inthe others, were identified. Clones that were found exclusively in oneof the three leukocyte libraries were also included in a list ofnucleotide sequences that could serve as lineage-specific markers.

Next, the sequence of the nucleotide sequences identified in PubMed orBodyMap were searched in Unigene (version 133), and a human Unigenecluster number was identified for each nucleotide sequence. The clusternumber was recorded in a Microsoft Excel™ spreadsheet, and anon-redundant list of these clones was made by sorting the clones byUniGene number, and removing all redundant clones using Microsoft Excel™tools. The non-redundant list of UniGene cluster numbers was thencompared to the UniGene cluster numbers of the cDNAs identified usingdifferential cDNA hybridization, as described above in Example 1 (listedin Table 3, Tables 8, 11-12, 14 and the sequence listing). Only UniGeneclusters that were not contained in the cDNA libraries were retained.Unigene clusters corresponding to 1911 candidate nucleotide sequencesfor leukocyte expression profiling were identified in this way and arelisted in Table 3 and the sequence listing.

DNA clones corresponding to each UniGene cluster number are obtained ina variety of ways. First, a cDNA clone with identical sequence to partof, or all of the identified UniGene cluster is bought from a commercialvendor or obtained from the IMAGE consortium (http://imagellnl.gov/, theIntegrated Molecular Analysis of Genomes and their Expression).Alternatively, PCR primers are designed to amplify and clone any portionof the nucleotide sequence from cDNA or genomic DNA using well-knowntechniques. Alternatively, the sequences of the identified UniGeneclusters are used to design and synthesize oligonucleotide probes foruse in microarray based expression profiling.

Example 3 DNA Sequencing and Processing of Raw Sequence Data

Clones of differentially expressed cDNAs (identified by subtractivehybridization, described above) were sequenced on an MJ ResearchBaseStation™ slab gel based fluorescent detection system, using BigDye™(Applied Biosystems, Foster City, Calif.) terminator chemistry was used(Heiner et al., Genome Res 1998 May; 8(5):557-61).

The fluorescent profiles were analyzed using the Phred sequence analysisprogram (Ewing et al, (1998), Genome Research 8: 175-185). Analysis ofeach clone results in a one pass nucleotide sequence and a quality filecontaining a number for each base pair with a score based on theprobability that the determined base is correct. Each sequence files andits respective quality files were initially combined into single fastaformat (Pearson, W R. Methods Mol. Biol. 2000; 132:185-219),multi-sequence file with the appropriate labels for each clone in theheaders for subsequent automated analysis.

Initially, known sequences were analyzed by pair wise similaritysearching using the blastn option of the blastall program obtained fromthe National Center for Biological Information, National Library ofMedicine, National Institutes of Health (NCBI) to determine the qualityscore that produced accurate matching (Altschul S F, et al. J Mol. Biol.1990 Oct. 5; 215(3):403-10.). Empirically, it was determined that a rawscore of 8 was the minimum that contained useful information. Using asliding window average for 16 base pairs, an average score wasdetermined. The sequence was removed (trimmed) when the average scorefell below 8. Maximum reads were 950 nucleotides long.

Next, the sequences were compared by similarity matching against adatabase file containing the flanking vector sequences used to clone thecDNA, using the blastall program with the blastn option. All regions ofvector similarity were removed, or “trimmed” from the sequences of theclones using scripts in the GAWK programming language, a variation ofAWK (Aho A V et al, The Awk Programming Language (Addison-Wesley,Reading Mass., 1988); Robbins, A D, “Effective AWK Programming” (FreeSoftware Foundation, Boston Mass., 1997). It was found that the first 45base pairs of all the sequences were related to vector; these sequenceswere also trimmed and thus removed from consideration. The remainingsequences were then compared against the NCBI vector database (Kitts, P.A. et al. National Center for Biological Information, National Libraryof Medicine, National Institutes of Health, Manuscript in preparation(2001) using blastall with the blastn option. Any vector sequences thatwere found were removed from the sequences.

Messenger RNA contains repetitive elements that are found in genomicDNA. These repetitive elements lead to false positive results insimilarity searches of query mRNA sequences versus known mRNA and ESTdatabases. Additionally, regions of low information content (long runsof the same nucleotide, for example) also result in false positiveresults. These regions were masked using the program RepeatMasker2 foundat http://repeatmasker.genome.washington.edu (Smit, AFA & Green, P“RepeatMasker” athttp://ftp.genome.washington.edu/RM/RepeatMasker.html). The trimmed andmasked files were then subjected to further sequence analysis.

Example 4 Further Sequence Analysis of Novel Nucleotide SequencesIdentified by Subtractive Hybridization Screening

cDNA sequences were further characterized using BLAST analysis. TheBLASTN program was used to compare the sequence of the fragment to theUniGene, dbEST, and nr databases at NCBI (GenBank release 123.0; seeTable 5). In the BLAST algorithm, the expect value for an alignment isused as the measure of its significance. First, the cDNA sequences werecompared to sequences in Unigene. If no alignments were found with anexpect value less than 10⁻²⁵, the sequence was compared to the sequencesin the dbEST database using BLASTN. If no alignments were found with anexpect value less than 10⁻²⁵, the sequence was compared to sequences inthe nr database.

The BLAST analysis produced the following categories of results: a) asignificant match to a known or predicted human gene, b) a significantmatch to a nonhuman DNA sequence, such as vector DNA or E. coli DNA, c)a significant match to an unidentified GenBank entry (a sequence notpreviously identified or predicted to be an expressed sequence or agene), such as a cDNA clone, mRNA, or cosmid, or d) no significantalignments. If a match to a known or predicted human gene was found,analysis of the known or predicted protein product was performed asdescribed below. If a match to an unidentified GenBank entry was found,or if no significant alignments were found, the sequence was searchedagainst all known sequences in the human genome database see Table 5).

If many unknown sequences were to be analyzed with BLASTN, theclustering algorithm CAP2 (Contig Assembly Program, version 2) was usedto cluster them into longer, contiguous sequences before performing aBLAST search of the human genome. Sequences that can be grouped intocontigs are likely to be cDNA from expressed genes rather than vectorDNA, E. coli DNA or human chromosomal DNA from a noncoding region, anyof which could have been incorporated into the library. Clusteredsequences provide a longer query sequence for database comparisons withBLASTN, increasing the probability of finding a significant match to aknown gene. When a significant alignment was found, further analysis ofthe putative gene was performed, as described below. Otherwise, thesequence of the original cDNA fragment or the CAP2 contig is used todesign a probe for expression analysis and further approaches are takento identify the gene or predicted gene that corresponds to the cDNAsequence, including similarity searches of other databases, molecularcloning, and Rapid Amplification of cDNA Ends (RACE).

In some cases, the process of analyzing many unknown sequences withBLASTN was automated by using the BLAST network-client program blastc13,which was downloaded from ftp://ncbi.nlm.nih.gov/blast/network/netblast.

When a cDNA sequence aligned to the sequence of one or more chromosomes,a large piece of the genomic region around the loci was used to predictthe gene containing the cDNA. To do this, the contig corresponding tothe mapped locus, as assembled by the RefSeq project at NCBI, wasdownloaded and cropped to include the region of alignment plus 100,000bases preceding it and 100,000 bases following it on the chromosome. Theresult was a segment 200 kb in length, plus the length of the alignment.This segment, designated a putative gene, was analyzed using an exonprediction algorithm to determine whether the alignment area of theunknown sequence was contained within a region predicted to betranscribed (see Table 6).

This putative gene was characterized as follows: all of the exonscomprising the putative gene and the introns between them were taken asa unit by noting the residue numbers on the 200 kb+ segment thatcorrespond to the first base of the first exon and the last base of thelast exon, as given in the data returned by the exon predictionalgorithm. The truncated sequence was compared to the UniGene, dbEST,and nr databases to search for alignments missed by searching with theinitial fragment.

The predicted amino acid sequence of the gene was also analyzed. Thepeptide sequence of the gene predicted from the exons was used inconjunction with numerous software tools for protein analysis (see Table7). These were used to classify or identify the peptide based onsimilarities to known proteins, as well as to predict physical,chemical, and biological properties of the peptides, including secondaryand tertiary structure, flexibility, hydrophobicity, antigenicity(hydrophilicity), common domains and motifs, and localization within thecell or tissues. The peptide sequence was compared to protein databases,including SWISS-PROT, TrEMBL, GenPept, PDB, PIR, PROSITE, Propom,PROSITE, Blocks, PRINTS, and Pfam, using BLASTP and other algorithms todetermine similarities to known proteins or protein subunits.

Example 5 Further Sequence Analysis of Novel Clone 596H6

The sequence of clone 596H6 is provided below:

(SEQ ID NO: 8767) ACTATATTTA GGCACCACTG CCATAAACTA CCAAAAAAAA AATGTAATTC50 CTAGAAGCTG TGAAGAATAG TAGTGTAGCT AAGCACGGTG TGTGGACAGT 100 GGGACATCTGCCACCTGCAG TAGGTCTCTG CACTCCCAAA AGCAAATTAC 150 ATTGGCTTGA ACTTCAGTATGCCCGGTTCC ACCCTCCAGA AACTTTTGTG 200 TTCTTTGTAT AGAATTTAGG AACTTCTGAGGGCCACAAAT ACACACATTA 250 AAAAAGGTAG AATTTTTGAA GATAAGATTC TTCTAAAAAAGCTTCCCAAT 300 GCTTGAGTAG AAAGTATCAG TAGAGGTATC AAGGGAGGAG AGACTAGGTG350 ACCACTAAAC TCCTTCAGAC TCTTAAAATT ACGATTCTTT TCTCAAAGGG 400GAAGAACGTC AGTGCAGCGA TCCCTTCACC TTTAGCTAAA GAATTGGACT 450 GTGCTGCTCAAAATAAAGAT CAGTTGGAGG TANGATGTCC AAGACTGAAG 500 GTAAAGGACT AGTGCAAACTGAAAGTGATG GGGAAACAGA CCTACGTATG 550 GAAGCCATGT AGTGTTCTTC ACAGGCTGCTGTTGACTGAA ATTCCTATCC 600 TCAAATTACT CTAGACTGAA GCTGCTTCCC TTCAGTGAGCAGCCTCTCCT 650 TCCAAGATTC TGGAAAGCAC ACCTGACTCC AAACAAAGAC TTAGAGCCCT700 GTGTCAGTGC TGCTGCTGCT TTTACCAGAT TCTCTAACCT TCCGGGTAGA 750 AGAG

This sequence was used as input for a series of BLASTN searches. First,it was used to search the UniGene database, build 132 at the web sitelocated at ncbi.nlm.nih.gov/BLAST/). No alignments were found with anexpect value less than the threshold value of 10⁻²⁵. A BLASTN search ofthe database dbEST, release 041001, was then performed on the sequenceand 21 alignments were found a the web site ncbi.nlm.nih.gov/BLAST/).Ten of these had expect values less than 10⁻²⁵, but all were matches tounidentified cDNA clones. Next, the sequence was used to run a BLASTNsearch of the nr database, release 123.0. No significant alignment toany sequence in nr was found. Finally, a BLASTN search of the humangenome was performed on the sequence located at the web sitecbi.nlm.nih.gov/genome/seq/page.cgi?F=HsBlast.html&&ORG=Hs.

A single alignment to the genome was found on contig NT_(—)004698.3(e=0.0). The region of alignment on the contig was from base 1,821,298to base 1,822,054, and this region was found to be mapped to chromosome1, from base 105,552,694 to base 105,553,450. The sequence containingthe aligned region, plus 100 kilobases on each side of the alignedregion, was downloaded. Specifically, the sequence of chromosome 1 frombase 105,452,694 to 105,653,450 was downloaded from the web site locatedatncbi.nlm.nih.gov/cgi-bin/Entrez/seq_reg.cgi?chr=1&from=105452694&to=105653450).

This 200,757 bp segment of the chromosome was used to predict exons andtheir peptide products as follows. The sequence was used as input forthe Genscan algorithm, using the following Genscan settings:

Organism: vertebrate

Suboptimal exon cutoff: 1.00 (no suboptimal exons)

Print options: Predicted CDS and peptides

The region matching the sequence of clone 596H6 was known to span basenumbers 100,001 to 100,757 of the input sequence. An exon was predictedby the algorithm, with a probability of 0.695, covering bases 100,601 to101,094 (designated exon 4.14 of the fourth predicted gene). This exonwas part of a predicted cistron that is 24,195 bp in length. Thesequence corresponding to the cistron was noted and saved separatelyfrom the 200,757 bp segment. BLASTN searches of the Unigene, dbEST, andnr databases were performed on it.

At least 100 significant alignments to various regions of the sequencewere found in the dbEST database, although most appeared to be redundantrepresentations of a few exons. All matches were to unnamed cDNAs andmRNAs (unnamed cDNAs and mRNAs are cDNAs and mRNAs not previouslyidentified, or shown to correspond to a known or predicted human gene)from various tissue types. Most aligned to a single region on thesequence and spanned 500 bp or less, but several consisted of five orsix regions separated by gaps, suggesting the locations of exons in thegene. Several significant matches to entries in the UniGene databasewere found, as well, even after masking low-complexity regions and shortrepeats in the sequence. All matches were to unnamed cDNA clones.

At least 100 significant alignments were found in the nr database, aswell. A similarity to hypothetical protein FLJ22457 (UniGene clusterHs.238707) was found (e=0.0). The cDNA of this predicted protein hasbeen isolated from B lymphocytes located at the web sitencbi.nlm.nih.gov/entrez/viewer.cgi?save=0&cmd=&cfm=on&f=1&view=gp&txt=0&val=13637988).

Other significant alignments were to unnamed cDNAs and mRNAs.

Using Genscan, the following 730 residue peptide sequence was predictedfrom the putative gene:

SEQ ID NO: 8768 MDGLGRRLRA SLRLKRGHGG HWRLNEMPYM KHEFDGGPPQ DNSGEALKEP50 ERAQEHSLPN FAGGQHFFEY LLVVSLKKKR SEDDYEPIIT YQFPKRENLL 100 RGQQEEEERLLKAIPLFCFP DGNEWASLTE YPSLSCKTPG LLAALVVEKA 150 QPRTCCHASA PSAAPQARGPDAPSPAAGQA LPAGPGPRLP KVYCIISCIG 200 CFGLFSKILD EVEKRHQISM AVIYPFMQGLREAAFPAPGK TVTLKSFIPD 250 SGTEFISLTR PLDSHLEHVD FSSLLHCLSF EQILQIFASAVLERKIIFLA 300 EGLREEEKDV RDSTEVRGAG ECHGFQRKGN LGKQWGLCVE DSVKMGDNQR350 GTSCSTLSQC IHAAAALLYP FSWAHTYIPV VPESLLATVC CPTPFMVGVQ 400MRFQQEVMDS PMEEIQPQAE IKTVNPLGVY EERGPEKASL CLFQVLLVNL 450 CEGTFLMSVGDEKDILPPKL QDDILDSLGQ GINELKTAEQ INEHVSGPFV 500 QFFVKIVGHY ASYIKREANGQGHFQERSFC KALTSKTNRR FVKKFVKTQL 550 FSLFIQEAEK SKNPPAEVTQ VGNSSTCVVDTWLEAAATAL SHHYNIFNTE 600 HTLWSKGSAS LHEVCGHVRT RVKRKILFLY VSLAFTMGKSIFLVENKAMN 650 MTIKWTTSGR PGHGDMFGVI ESWGAAALLL LTGRVRDTGK SSSSTGHRAS700 KSLVWSQVCF PESWEERLLT EGKQLQSRVI

Multiple analyses were performed using this prediction. First, apairwise comparison of the sequence above and the sequence of FLJ22457,the hypothetical protein mentioned above, using BLASTP version 2.1.2,resulted in a match with an expect value of 0.0. The peptide sequencepredicted from clone 596H6 was longer and 19% of the region of alignmentbetween the two resulted from gaps in hypothetical protein FLJ22457. Thecause of the discrepancy might be alternative mRNA splicing, alternativepost-translational processing, or differences in the peptide-predictingalgorithms used to create the two sequences, but the homology betweenthe two is significant.

BLASTP and TBLASTN were also used to search for sequence similarities inthe SWISS-PROT, TrEMBL, GenBank Translated, and PDB databases. Matchesto several proteins were found, among them a tumor cell suppressionprotein, HTS1. No matches aligned to the full length of the peptidesequence, however, suggesting that similarity is limited to a fewregions of the peptide.

TBLASTN produced matches to several proteins—both identified andtheoretical—but again, no matches aligned to the full length of thepeptide sequence. The best alignment was to the same hypotheticalprotein found in GenBank before (FLJ22457).

To discover similarities to protein families, comparisons of the domains(described above) were carried out using the Pfam and Blocks databases.A search of the Pfam database identified two regions of the peptidedomains as belonging the DENN protein family (e=2.1×10−⁻³³). The humanDENN protein possesses an RGD cellular adhesion motif and aleucine-zipper-like motif associated with protein dimerization, andshows partial homology to the receptor binding domain of tumor necrosisfactor alpha. DENN is virtually identical to MADD, a human MAPkinase-activating death domain protein that interacts with type I tumornecrosis factor receptor(http://srs.ebi.ac.uk/srs6bin/cgi-bin/wgetz?-id+fS5n1GQsHf+-e+[INTERPRO:‘IPR001194’]).The search of the Blocks database also revealed similarities betweenregions of the peptide sequence and known protein groups, but none witha satisfactory degree of confidence. In the Blocks scoring system,scores over 1,100 are likely to be relevant. The highest score of anymatch to the predicted peptide was 1,058.

The Prosite, Propom, PRINTS databases (all publicly available) were usedto conduct further domain and motif analysis. The Prosite searchgenerated many recognized protein domains. A BLASTP search was performedto identify areas of similarity between the protein query sequence andPRINTS, a protein database of protein fingerprints, groups of motifsthat together form a characteristic signature of a protein family. Inthis case, no groups were found to align closely to any section of thesubmitted sequence. The same was true when the Propom database wassearched with BLASTP.

A prediction of protein structure was done by performing a BLAST searchof the sequence against PDB, a database in which every member hastertiary structure information. No significant alignments were found bythis method. Secondary and super-secondary structure was examined usingthe Garnier algorithm. Although it is only considered to be 60-65%accurate, the algorithm provided information on the locations andlengths of alpha-helices, beta-sheets, turns and coils.

The antigenicity of the predicted peptide was modeled by graphinghydrophilicity vs. amino acid number. This produced a visualrepresentation of trends in hydrophilicity along the sequence. Manylocations in the sequence showed antigenicity and five sites hadantigenicity greater than 2. This information can be used in the designof affinity reagents to the protein.

Membrane-spanning regions were predicted by graphing hydrophobicity vs.amino acid number. Thirteen regions were found to be somewhathydrophobic. The algorithm TMpred predicted a model with 6 strongtransmembrane heliceslocated at the web site at ch.embnet.org/software/

TMPRED_form.html).

NNPSL is a neural network algorithm developed by the Sanger Center. Ituses amino acid composition and sequence to predict cellular location.For the peptide sequence submitted, its first choice was mitochondrial(51.1% expected accuracy). Its second choice was cytoplasmic (91.4%expected accuracy).

Example 6 Further Sequence Analysis of Novel Clone 486E11

The sequence of clone 486E11 is provided below:

SEQ ID NO: 8769 TAAAAGCAGG CTGTGCACTA GGGACCTAGT GACCTTACTA GAAAAAACTC50 AAATTCTCTG AGCCACAAGT CCTCATGGGC AAAATGTAGA TACCACCACC 100 TAACCCTGCCAATTTCCTAT CATTGTGACT ATCAAATTAA ACCACAGGCA 150 GGAAGTTGCC TTGAAAACTTTTTATAGTGT ATATTACTGT TCACATAGAT 200 NAGCAATTAA CTTTACATAT ACCCGTTTTTAAAAGATCAG TCCTGTGATT 250 AAAAGTCTGG CTGCCCTAAT TCACTTCGAT TATACATTAGGTTAAAGCCA 300 TATAAAAGAG GCACTACGTC TTCGGAGAGA TGAATGGATA TTACAAGCAG350 TAATGTTGGC TTTGGAATAT ACACATAATG TCCACTTGAC CTCATCTATT 400TGACACAAAA TGTAAACTAA ATTATGAGCA TCATTAGATA CCTTGGCCTT 450 TTCAAATCACACAGGGTCCT AGATCTNNNN NNNNNNNNNN NNNNNNNNNN 500 NNNNNNNNNN NNNNNNNNNNNNNNNNNNNN NNNNNNNNAC TTTGGGATTC 550 CTATATCTTT GTCAGCTGTC AACTTCAGTGTTTTCAGGTT AAATTCTATC 600 CATAGTCATC CCAATATACC TGCTTTAGAT GATACAACCTTCAAAAGATC 650 CGCTCTTCCT CGTAAAAAGT GGAG

The BLASTN program was used to compare the sequence to the UniGene anddbEST databases. No significant alignments were found in either. It wasthen searched against the nr database and only alignments to unnamedgenomic DNA clones were found.

CAP2 was used to cluster a group of unknowns, including clone 486E11.The sequence for 486E11 was found to overlap others. These formed acontig of 1,010 residues, which is shown below:

SEQ ID NO: 8832 CGGACAGGTA CCTAAAAGCA GGCTGTGCAC TAGGGACCTA GTGACCTTAC50 TAGAAAAAAC TCAAATTCTC TGAGCCACAA GTCCTCATGG GCAAAATGTA 100 GATACCACCACCTAACCCTG CCAATTTCCT ATCATTGTGA CTATCAAATT 150 AAACCACAGG CAGGAAGTTGCCTTGAAAAC TTTTTATAGT GTATATTACT 200 GTTCACATAG ATNAGCAATT AACTTTACATATACCCGTTT TTAAAAGATC 250 AGTCCTGTGA TTAAAAGTCT GGCTGCCCTA ATTCACTTCGATTATACATT 300 AGGTTAAAGC CATATAAAAG AGGCACTACG TCTTCGGAGA GATGAATGGA350 TATTACAAGC AGTAATTTTG GCTTTGGAAT ATACACATAA TGTCCACTTG 400ACCTCATCTA TTTGACACAA AATGTAAACT AAATTATGAG CATCATTAGA 450 TACCTTGGGCCTTTTCAAAT CACACAGGGT CCTAGATCTG NNNNNNNNNN 500 NNNNNNNNNN NNNNNNNNNNNNNNNNNNNN NNNNNNNNNN NNNNNNNNNN 550 NACTTTGGAT TCTTATATCT TTGTCAGCTGTCAACTTCAG TGTTTTCAGG 600 NTAAATTCTA TCCATAGTCA TCCCAATATA CCTGCTTTAGATGATACAAA 650 CTTCAAAAGA TCCGGCTCTC CCTCGTAAAA CGTGGAGGAC AGACATCAAG700 GGGGTTTTCT GAGTAAAGAA AGGCAACCGC TCGGCAAAAA CTCACCCTGG 750CACAACAGGA NCGAATATAT ACAGACGCTG ATTGAGCGTT TTGCTCCATC 800 TTCACTTCTGTTAAATGAAG ACATTGATAT CTAAAATGCT ATGAGTCTAA 850 CTTTGTAAAA TTAAAATAGATTTGTAGTTA TTTTTCAAAA TGAAATCGAA 900 AAGATACAAG TTTTGAAGGC AGTCTCTTTTTCCACCCTGC CCCTCTAGTG 950 TGTTTTACAC ACTTCTCTGG CCACTCCAAC AGGGAAGCTGGTCCAGGGCC 1000 ATTATACAGG

The sequence of the CAP2 contig was used in a BLAST search of the humangenome. 934 out of 1,010 residues aligned to a region of chromosome 21.A gap of 61 residues divided the aligned region into two smallerfragments. The sequence of this region, plus 100 kilobases on each sideof it, was downloaded and analyzed using the Genscan site at MIT, withthe following settings:

Organism: vertebrate

Suboptimal exon cutoff: 1.00 (no suboptimal exons)

Print options: Predicted CDS and peptides

The fragment was found to fall within one of several predicted genes inthe chromosome region. The bases corresponding to the predicted gene,including its predicted introns, were saved as a separate file and usedto search GenBank again with BLASTN to find any ESTs or UniGene clustersidentified by portions of the sequence not included in the originalunknown fragment. The nr database contained no significant matches. Atleast 100 significant matches to various parts of the predicted genewere found in the dbEST database, but all of them were to unnamed cDNAclones. Comparison to UniGene produced fewer significant matches, butall matches were to unnamed cDNAs.

The peptide sequence predicted by Genscan was also saved. Multiple typesof analyses were performed on it using the resources mentioned in Table3. BLASTP and TBLASTN were used to search the TrEMBL protein database atthe web site expasy.ch/sprot and the GenBank nr database located at theweb site ncbi.nlm.hih.gov/BLAST/), which includes data from theSwissProt, PR, PRF, and PDB databases. No significant matches were foundin any of these, so no gene identity or tertiary structure wasdiscovered.

The peptide sequence was also searched for similarity to known domainsand motifs using BLASTP with the Prosite, Blocks, Pfam, and Propomdatabases. The searches produced no significant alignments to knowndomains. BLASTP comparison to the PRINTS database produced an alignmentto the P450 protein family, but with a low probability of accuracy(e=6.9).

Two methods were used to predict secondary structure—theGarnier/Osguthorpe/Robson model and the Chou-Fasman model. The twomethods differed somewhat in their results, but both producedrepresentations of the peptide sequence with helical and sheet regionsand locations of turns.

Antigenicity was plotted as a graph with amino acid number in thesequence on the x-axis and hydrophilicity on the y-axis. Several areasof antigenicity were observed, but only one with antigenicity greaterthan 2. Hydrophobicity was plotted in the same way. Only one region,from approximately residue 135 to residue 150, had notablehydrophobicity. TMpred, accessed through ExPASy, was used to predicttransmembrane helices. No regions of the peptide sequence were predictedwith reasonable confidence to be membrane-spanning helices.

NNPSL predicted that the putative protein would be found either in thenucleus (expected prediction accuracy=51.1%) or secreted from the cell(expected prediction accuracy=91.4%).

Example 7 Preparation of a Leukocyte cDNA Array Comprising a CandidateGene Library

Candidate genes and gene sequences for leukocyte expression profilingwere identified through methods described elsewhere in this document.Candidate genes are used to obtain or design probes for peripheralleukocyte expression profiling in a variety of ways.

A cDNA microarray carrying 384 probes was constructed using sequencesselected from the cDNA libraries described in example 1. cDNAs wereselected from T-cell libraries, PBMC libraries and buffy coat libraries.A listing of the cDNA fragments used is given in Table 8.

96-Well PCR

Plasmids were isolated in 96-well format and PCR was performed in96-well format. A master mix was made that contain the reaction buffer,dNTPs, forward and reverse primer and DNA polymerase was made. 99 ul ofthe master mix was aliquoted into 96-well plate. 1 ul of plasmid (1-2ng/ul) of plasmid was added to the plate. The final reactionconcentration was 10 mM Tris pH 8.3, 3.5 mM MgCl₂, 25 mM KCl, 0.4 mMdNTPs, 0.4 uM M13 forward primer, 0.4 M13 reverse primer, and 10 U ofTaq Gold (Applied Biosystems). The PCR conditions were:

Step 1 95 C for 10 min

Step 2 95 C for 15 sec

Step 3 56 C for 30 sec

Step 4 72 C for 2 min 15 seconds

Step 5 go to Step 2 39 times

Step 6 72 C for 10 minutes

Step 7 4 C for ever.

PCR Purification

PCR purification was done in a 96-well format. The Arraylt (TelechemInternational, Inc.) PCR purification kit was used and the providedprotocol was followed without modification. Before the sample wasevaporated to dryness, the concentration of PCR products was determinedusing a spectrophotometer. After evaporation, the samples werere-suspended in 1× Micro Spotting Solution (Arraylt) so that themajority of the samples were between 0.2-1.0 ug/ul.

Array Fabrication

Spotted cDNA microarrays were then made from these PCR products byArraylt using their protocols. Each fragment was spotted 3 times ontoeach array.

Candidate genes and gene sequences for leukocyte expression profilingwere identified through methods described elsewhere in this document.Those candidate genes are used for peripheral leukocyte expressionprofiling. The candidate libraries can used to obtain or design probesfor expression profiling in a variety of ways.

Oligonucleotide probes are also prepared using the DNA sequenceinformation for the candidate genes identified by differentialhybridization screening (listed in Table 3 and the sequence listing)and/or the sequence information for the genes identified by databasemining (listed in Table 2) is used to design complimentaryoligonucleotide probes. Oligo probes are designed on a contract basis byvarious companies (for example, Compugen, Mergen, Affymetrix, Telechem),or designed from the candidate sequences using a variety of parametersand algorithms as indicated at located at the web sitegenome.wi.mit.edu/cgi-bin/primer/primer3.cgi. Briefly, the length of theoligonucleotide to be synthesized is determined, preferably greater than18 nucleotides, generally 18-24 nucleotides, 24-70 nucleotides and, insome circumstances, more than 70 nucleotides. The sequence analysisalgorithms and tools described above are applied to the sequences tomask repetitive elements, vector sequences and low complexity sequences.Oligonucleotides are selected that are specific to the candidatenucleotide sequence (based on a Blast n search of the oligonucleotidesequence in question against gene sequences databases, such as the HumanGenome Sequence, UniGene, dbEST or the non-redundant database at NCBI),and have <50% G content and 25-70% G+C content. Desired oligonucleotidesare synthesized using well-known methods and apparatus, or ordered froma company (for example Sigma). Oligonucleotides are spotted ontomicroarrays. Alternatively, oligonucleotides are synthesized directly onthe array surface, using a variety of techniques (Hughes et al. 2001,Yershov et al. 1996, Lockhart et al 1996).

Example 8 Preparation of RNA from Mononuclear Cells for ExpressionProfiling

Blood was isolated from the subject for leukocyte expression profilingusing the following methods:

Two tubes were drawn per patient. Blood was drawn from either a standardperipheral venous blood draw or directly from a large-boreintra-arterial or intravenous catheter inserted in the femoral artery,femoral vein, subclavian vein or internal jugular vein. Care was takento avoid sample contamination with heparin from the intravascularcatheters, as heparin can interfere with subsequent RNA reactions.

For each tube, 8 ml of whole blood was drawn into a tube (CPT,Becton-Dickinson order #362753) containing the anticoagulant Citrate,25° C. density gradient solution (e.g. Ficoll, Percoll) and a polyestergel barrier that upon centrifugation was permeable to RBCs andgranulocytes but not to mononuclear cells. The tube was inverted severaltimes to mix the blood with the anticoagulant. The tubes werecentrifuged at 1750×g in a swing-out rotor at room temperature for 20minutes. The tubes were removed from the centrifuge and inverted 5-10times to mix the plasma with the mononuclear cells, while trapping theRBCs and the granulocytes beneath the gel barrier. Theplasma/mononuclear cell mix was decanted into a 15 ml tube and 5 ml ofphosphate-buffered saline (PBS) is added. The 15 ml tubes were spun for5 minutes at 1750×g to pellet the cells. The supernatant was discardedand 1.8 ml of RLT lysis buffer is added to the mononuclear cell pellet.The buffer and cells were pipetted up and down to ensure complete lysisof the pellet. The cell lysate was frozen and stored until it isconvenient to proceed with isolation of total RNA.

Total RNA was purified from the lysed mononuclear cells using the QiagenRneasy Miniprep kit, as directed by the manufacturer (10/99 version) fortotal RNA isolation, including homogenization (Qiashredder columns) andon-column DNase treatment. The purified RNA was eluted in 50 ul ofwater. The further use of RNA prepared by this method is described inExample 11, 24, and 23.

Some samples were prepared by a different protocol, as follows:

Two 8 ml blood samples were drawn from a peripheral vein into a tube(CPT, Becton-Dickinson order #362753) containing anticoagulant(Citrate), 25° C. density gradient solution (Ficoll) and a polyester gelbarrier that upon centrifugation is permeable to RBCs and granulocytesbut not to mononuclear cells. The mononuclear cells and plasma remainedabove the barrier while the RBCs and granulocytes were trapped below.The tube was inverted several times to mix the blood with theanticoagulant, and the tubes were subjected to centrifugation at 1750×gin a swing-out rotor at room temperature for 20 min. The tubes wereremoved from the centrifuge, and the clear plasma layer above the cloudymononuclear cell layer was aspirated and discarded. The cloudymononuclear cell layer was aspirated, with care taken to rinse all ofthe mononuclear cells from the surface of the gel barrier with PBS(phosphate buffered saline). Approximately 2 mls of mononuclear cellsuspension was transferred to a 2 ml microcentrifuge tube, andcentrifuged for 3 min. at 16,000 rpm in a microcentrifuge to pellet thecells. The supernatant was discarded and 1.8 ml of RLT lysis buffer(Qiagen) were added to the mononuclear cell pellet, which lysed thecells and inactivated Rnases. The cells and lysis buffer were pipettedup and down to ensure complete lysis of the pellet. Cell lysate wasfrozen and stored until it was convenient to proceed with isolation oftotal RNA.

RNA samples were isolated from 8 mL of whole blood. Yields ranged from 2ug to 20 ug total RNA for 8 mL blood. A260/A280 spectrophotometricratios were between 1.6 and 2.0, indicating purity of sample. 2 ul ofeach sample were run on an agarose gel in the presence of ethidiumbromide. No degradation of the RNA sample and no DNA contamination wasvisible.

In some cases, specific subsets of mononuclear cells were isolated fromperipheral blood of human subjects. When this was done, the StemSep cellseparation kits (manual version 6.0.0) were used from StemCellTechnologies (Vancouver, Canada). This same protocol can be applied tothe isolation of T cells, CD4 T cells, CD8 T cells, B cells, monocytes,NK cells and other cells. Isolation of cell types using negativeselection with antibodies may be desirable to avoid activation of targetcells by antibodies.

Example 9 Preparation of Universal Control RNA for Use in LeukocyteExpression Profiling

Control RNA was prepared using total RNA from Buffy coats and/or totalRNA from enriched mononuclear cells isolated from Buffy coats, both withand without stimulation with ionomycin and PMA. The following controlRNAs were prepared:

Control 1: Buffy Coat Total RNA

Control 2: Mononuclear cell Total RNA

Control 3: Stimulated buffy coat Total RNA

Control 4: Stimulated mononuclear Total RNA

Control 5: 50% Buffy coat Total RNA/50% Stimulated buffy coat Total RNA

Control 6: 50% Mononuclear cell Total RNA/50% Stimulated MononuclearTotal RNA

Some samples were prepared using the following protocol: Buffy coatsfrom 38 individuals were obtained from Stanford Blood Center. Each buffycoat is derived from ˜350 mL whole blood from one individual. 10 mlbuffy coat was removed from the bag, and placed into a 50 ml tube. 40 mlof Buffer EL (Qiagen) was added, the tube was mixed and placed on icefor 15 minutes, then cells were pelleted by centrifugation at 2000×g for10 minutes at 4° C. The supernatant was decanted and the cell pellet wasre-suspended in 10 ml of Qiagen Buffer EL. The tube was then centrifugedat 2000×g for 10 minutes at 4° C. The cell pellet was then re-suspendedin 20 ml TRIZOL (GibcoBRL) per Buffy coat sample, the mixture wasshredded using a rotary homogenizer, and the lysate was then frozen at−80° C. prior to proceeding to RNA isolation.

Other control RNAs were prepared from enriched mononuclear cellsprepared from Buffy coats. Buffy coats from Stanford Blood Center wereobtained, as described above. 10 ml buffy coat was added to a 50 mlpolypropylene tube, and 10 ml of phosphate buffer saline (PBS) was addedto each tube. A polysucrose (5.7 g/dL) and sodium diatrizoate (9.0 g/dL)solution at a 1.077+/−0.0001 g/ml density solution of equal volume todiluted sample was prepared (Histopaque 1077, Sigma cat. no 1077-1).This and all subsequent steps were performed at room temperature. 15 mlof diluted buffy coat/PBS was layered on top of 15 ml of the histopaquesolution in a 50 ml tube. The tube was centrifuged at 400×g for 30minutes at room temperature. After centrifugation, the upper layer ofthe solution to within 0.5 cm of the opaque interface containing themononuclear cells was discarded. The opaque interface was transferredinto a clean centrifuge tube. An equal volume of PBS was added to eachtube and centrifuged at 350×g for 10 minutes at room temperature. Thesupernatant was discarded. 5 ml of Buffer EL (Qiagen) was used toresuspend the remaining cell pellet and the tube was centrifuged at2000×g for 10 minutes at room temperature. The supernatant wasdiscarded. The pellet was resuspended in 20 ml of TRIZOL (GibcoBRL) foreach individual buffy coat that was processed. The sample washomogenized using a rotary homogenizer and frozen at −80 C until RNA wasisolated.

RNA was isolated from frozen lysed Buffy coat samples as follows: frozensamples were thawed, and 4 ml of chloroform was added to each buffy coatsample. The sample was mixed by vortexing and centrifuged at 2000×g for5 minutes. The aqueous layer was moved to new tube and then repurifiedby using the RNeasy Maxi RNA clean up kit, according to themanufacturer's instruction (Qiagen, PN 75162). The yield, purity andintegrity were assessed by spectrophotometer and gel electrophoresis.

Some samples were prepared by a different protocol, as follows. Thefurther use of RNA prepared using this protocol is described in Example23.

50 whole blood samples were randomly selected from consented blooddonors at the Stanford Medical School Blood Center. Each buffy coatsample was produced from ˜350 mL of an individual's donated blood. Thewhole blood sample was centrifuged at ˜4,400×g for 8 minutes at roomtemperature, resulting in three distinct layers: a top layer of plasma,a second layer of buffy coat, and a third layer of red blood cells. 25ml of the buffy coat fraction was obtained and diluted with an equalvolume of PBS (phosphate buffered saline). 30 ml of diluted buffy coatwas layered onto 15 ml of sodium diatrizoate solution adjusted to adensity of 1.077+/−0.001 g/ml (Histopaque 1077, Sigma) in a 50 mLplastic tube. The tube was spun at 800 g for 10 minutes at roomtemperature. The plasma layer was removed to the 30 ml mark on the tube,and the mononuclear cell layer removed into a new tube and washed withan equal volume of PBS, and collected by centrifugation at 2000 g for 10minutes at room temperature. The cell pellet was resuspended in 10 ml ofBuffer EL (Qiagen) by vortexing and incubated on ice for 10 minutes toremove any remaining erthythrocytes. The mononuclear cells were spun at2000 g for 10 minutes at 4 degrees Celsius. The cell pellet was lysed in25 ml of a phenol/guanidinium thiocyanate solution (TRIZOL Reagent,Invitrogen). The sample was homogenized using a PowerGene 5 rotaryhomogenizer (Fisher Scientific) and Omini disposable generator probes(Fisher Scientific). The Trizol lysate was frozen at −80 degrees C.until the next step.

The samples were thawed out and incubated at room temperature for 5minutes. 5 ml chloroform was added to each sample, mixed by vortexing,and incubated at room temperature for 3 minutes. The aqueous layers weretransferred to new 50 ml tubes. The aqueous layer containing total RNAwas further purified using the Qiagen RNeasy Maxi kit (PN 75162), perthe manufacturer's protocol (October 1999). The columns were elutedtwice with 1 ml Rnase-free water, with a minute incubation before eachspin. Quantity and quality of RNA was assessed using standard methods.Generally, RNA was isolated from batches of 10 buffy coats at a time,with an average yield per buffy coat of 870 μg, and an estimated totalyield of 43.5 mg total RNA with a 260/280 ratio of 1.56 and a 28S/18Sratio of 1.78.

Quality of the RNA was tested using the Agilent 2100 Bioanalyzer usingRNA 6000 microfluidics chips. Analysis of the electrophorgrams from theBioanalyzer for five different batches demonstrated the reproducibilityin quality between the batches.

Total RNA from all five batches were combined and mixed in a 50 ml tube,then aliquoted as follows: 2×10 ml aliquots in 15 ml tubes, and the restin 100 μl aliquots in 1.5 ml microcentrifuge tubes. The aliquots gavehighly reproducible results with respect to RNA purity, size andintegrity. The RNA was stored at −80° C.

Test Hybridization of Reference RNA.

When compared with BC38 and Stimulated mononuclear reference samples,the R50 performed as well, if not better than the other referencesamples as shown in FIG. 4. In an analysis of hybridizations, where theR50 targets were fluorescently labeled with Cy-5 using methods describedherein and the amplified and labeled aRNA was hybridized (as in example23) to the olignoucleotide array described in examples 20-22. The R50detected 97.3% of probes with a Signal to Noise ratio (S/N) of greaterthan three and 99.9% of probes with S/N greater than one.

Example 10 RNA Labeling and Hybridization to a Leukocyte cDNA Array ofCandidate Nucleotide Sequences

Comparison of Guanine-Silica to Acid-Phenol RNA Purification (GSvsAP)

These data are from a set of 12 hybridizations designed to identifydifferences between the signal strength from two different RNApurification methods. The two RNA methods used were guanidine-silica(GS, Qiagen) and acid-phenol (AP, Trizol, Gibco BRL). Ten tubes of bloodwere drawn from each of four people. Two were used for the AP prep, theother eight were used for the GS prep. The protocols for the leukocyteRNA preps using the AP and GS techniques were completed as describedhere:

Guanidine-Silica (GS) Method:

For each tube, 8 ml blood was drawn into a tube containing theanticoagulant Citrate, 25° C. density gradient solution and a polyestergel barrier that upon centrifugation is permeable to RBCs andgranulocytes but not to mononuclear cells. The mononuclear cells andplasma remained above the barrier while the RBCs and granulocytes weretrapped below. CPT tubes from Becton-Dickinson (#362753) were used forthis purpose. The tube was inverted several times to mix the blood withthe anticoagulant. The tubes were immediately centrifuged @ 1750×g in aswinging bucket rotor at room temperature for 20 min. The tubes wereremoved from the centrifuge and inverted 5-10 times. This mixed theplasma with the mononuclear cells, while the RBCs and the granulocytesremained trapped beneath the gel barrier. The plasma/mononuclear cellmix was decanted into a 15 ml tube and 5 ml of phosphate-buffered saline(PBS) was added. The 15 ml tubes are spun for 5 minutes at 1750×g topellet the cells. The supernatant was discarded and 1.8 ml of RLT lysisbuffer (guanidine isothyocyanate) was added to the mononuclear cellpellet. The buffer and cells were pipetted up and down to ensurecomplete lysis of the pellet. The cell lysate was then processed exactlyas described in the Qiagen Rneasy Miniprep kit protocol (10/99 version)for total RNA isolation (including steps for homogenization (Qiashreddercolumns) and on-column DNase treatment. The purified RNA was eluted in50 ul of water.

Acid-Phenol (AP) Method:

For each tube, 8 ml blood was drawn into a tube containing theanticoagulant Citrate, 25° C. density gradient solution and a polyestergel barrier that upon centrifugation is permeable to RBCs andgranulocytes but not to mononuclear cells. The mononuclear cells andplasma remained above the barrier while the RBCs and granulocytes weretrapped below. CPT tubes from Becton-Dickinson (#362753) were used forthis purpose. The tube was inverted several times to mix the blood withthe anticoagulant. The tubes were immediately centrifuged @ 1750×g in aswinging bucket rotor at room temperature for 20 min. The tubes wereremoved from the centrifuge and inverted 5-10 times. This mixed theplasma with the mononuclear cells, while the RBCs and the granulocytesremained trapped beneath the gel barrier. The plasma/mononuclear cellmix was decanted into a 15 ml tube and 5 ml of phosphate-buffered saline(PBS) was added. The 15 ml tubes are spun for 5 minutes @ 1750×g topellet the cells. The supernatant was discarded and the cell pellet waslysed using 0.6 mL Phenol/guanidine isothyocyanate (e.g. Trizol reagent,GibcoBRL). Subsequent total RNA isolation proceeded using themanufacturers protocol.

RNA from each person was labeled with either Cy3 or Cy5, and thenhybridized in pairs to the mini-array. For instance, the first array washybridized with GS RNA from one person (Cy3) and GS RNA from a secondperson (Cy5).

Techniques for labeling and hybridization for all experiments discussedhere were completed as detailed above in example 10. Arrays wereprepared as described in example 7.

RNA isolated from subject samples, or control Buffy coat RNA, werelabeled for hybridization to a cDNA array. Total RNA (up to 100 μg) wascombined with 2 μl of 100 μM solution of an Oligo (dT) 12-18 (GibcoBRL)and heated to 70° C. for 10 minutes and place on ice. Reaction bufferwas added to the tube, to a final concentration of 1×RT buffer(GibcoBRL), 10 mM DTT (GibcoBRL), 0.1 mM unlabeled dATP, dTTP, and dGTP,and 0.025 mM unlabeled dCTP, 200 pg of CAB (A. thaliana photosystem Ichlorophyll a/b binding protein), 200 pg of RCA (A. thaliana RUBISCOactivase), 0.25 mM of Cy-3 or Cy-5 dCTP, and 400 U Superscript II RT(GibcoBRL).

The volumes of each component of the labeling reaction were as follows:20 μl of 5×RT buffer; 10 μl of 100 mM DTT; 1 μl of 10 mM dNTPs withoutdCTP; 0.5 μl of 5 mM CTP; 13 μl of H20; 0.02 μl of 10 ng/μl CAB and RCA;1 μl of 40 Units/μl RNAseOUT Recombinatnt Ribonuclease Inhibitor(GibcoBRL); 2.5 μl of 1.0 mM Cy-3 or Cy-5 dCTP; and 2.0 μl of 200Units/μl of Superscript II RT. The sample was vortexed and centrifuged.The sample was incubated at 4° C. for 1 hour for first strand cDNAsynthesis, then heated at 70° C. for 10 minutes to quench enzymaticactivity. 1 μA of 10 mg/ml of Rnase A was added to degrade the RNAstrand, and the sample was incubated at 37° C. for 30 minutes.

Next, the Cy-3 and Cy-5 cDNA samples were combined into one tube.Unincorporated nucleotides were removed using QIAquick RCR purificationprotocol (Qiagen), as directed by the manufacturer. The sample wasevaporated to dryness and resuspended in 5 μl of water. The sample wasmixed with hybridization buffer containing 5×SSC, 0.2% SDS, 2 mg/mlCot-1 DNA (GibcoBRL), 1 mg/ml yeast tRNA (GibcoBRL), and 1.6 ng/μl polydA40-60 (Pharmacia). This mixture was placed on the microarray surfaceand a glass cover slip was placed on the array (Corning). The microarrayglass slide was placed into a hybridization chamber (Arrraylt). Thechamber was then submerged in a water bath overnight at 62° C. Themicroarray was removed from the cassette and the cover slip was removedby repeatedly submerging it to a wash buffer containing 1×SSC, and 0.1%SDS. The microarray slide was washed in 1×SSC/0.1% SDS for 5 minutes.The slide was then washed in 0.1% SSC/0.1% SDS for 5 minutes. The slidewas finally washed in 0.1×SSC for 2 minutes. The slide was spun at 1000rpm for 2 minutes to dry out the slide, then scanned on a microarrayscanner (Axon Instruments, Union City, Calif.).

Six hybridizations with 20 μg of RNA were performed for each type of RNApreparation (GS or AP). Since both the Cy3 and the Cy5 labeled RNA arefrom test preparations, there are six data points for each GS prepped,Cy3-labeled RNA and six for each GS-prepped, Cy5-labeled RNA. The miniarray hybridizations were scanned on and Axon Instruments scanner usingGenPix 3.0 software. The data presented were derived as follows. First,all features flagged as “not found” by the software were removed fromthe dataset for individual hybridizations. These features are usuallydue to high local background or other processing artifacts. Second, themedian fluorescence intensity minus the background fluorescenceintensity was used to calculate the mean background subtracted signalfor each dye for each hybridization. In FIG. 4, the mean of these meansacross all six hybridizations is graphed (n=6 for each column). Theerror bars are the SEM. This experiment shows that the average signalfrom AP prepared RNA is 47% of the average signal from GS prepared RNAfor both Cy3 and Cy5.

Generation of Expression Data for Leukocyte Genes from PeripheralLeukocyte Samples

Six hybridizations were performed with RNA purified from human bloodleukocytes using the protocols given above. Four of the six wereprepared using the GS method and 2 were prepared using the AP method.Each preparation of leukocyte RNA was labeled with Cy3 and 10 μghybridized to the mini-array. A control RNA was batch labeled with Cy5and 10 μg hybridized to each mini-array together with the Cy3-labeledexperimental RNA.

The control RNA used for these experiments was Control 1: Buffy CoatRNA, as described above. The protocol for the preparation of that RNA isreproduced here:

Buffy Coat RNA Isolation:

Buffy coats were obtained from Stanford Blood Center (in total 38individual buffy coats were used. Each buffy coat is derived from ˜350mL whole blood from one individual. 10 ml buffy coat was taken andplaced into a 50 ml tube and 40 ml of a hypoclorous acid (HOCl) solution(Buffer EL from Qiagen) was added. The tube was mixed and placed on icefor 15 minutes. The tube was then centrifuged at 2000×g for 10 minutesat 4° C. The supernatant was decanted and the cell pellet wasre-suspended in 10 ml of hypochlorous acid solution (Qiagen Buffer EL).The tube was then centrifuged at 2000×g for 10 minutes at 4° C. The cellpellet was then re-suspended in 20 ml phenol/guanidine thiocyanatesolution (TRIZOL from GibcoBRL) for each individual buffy coat that wasprocessed. The mixture was then shredded using a rotary homogenizer. Thelysate was then frozen at −80° C. prior to proceeding to RNA isolation.

The arrays were then scanned and analyzed on an Axon Instruments scannerusing GenePix 3.0 software. The data presented were derived as follows.First, all features flagged as “not found” by the software were removedfrom the dataset for individual hybridizations. Second, control featureswere used to normalize the data for labeling and hybridizationvariability within the experiment. The control features are cDNA forgenes from the plant, Arabidopsis thaliana, that were included whenspotting the mini-array. Equal amounts of RNA complementary to two ofthese cDNAs were added to each of the samples before they were labeled.A third was pre-labeled and equal amounts were added to eachhybridization solution before hybridization. Using the signal from thesegenes, we derived a normalization constant (4) according to thefollowing formula:

$L_{j} = \frac{\frac{\sum\limits_{i = 1}^{N}{BGSS}_{j,i}}{N}}{\frac{\sum\limits_{j = 1}^{K}\frac{\sum\limits_{i = 1}^{N}{BGSS}_{j,i}}{N}}{K}}$

where BGSS, is the signal for a specific feature as identified in theGenePix software as the median background subtracted signal for thatfeature, N is the number of A. thaliana control features, K is thenumber of hybridizations, and L is the normalization constant for eachindividual hybridization.

Using the formula above, the mean over all control features of aparticular hybridization and dye (eg Cy3) was calculated. Then thesecontrol feature means for all Cy3 hybridizations were averaged. Thecontrol feature mean in one hybridization divided by the average of allhybridizations gives a normalization constant for that particular Cy3hybridization.

The same normalization steps were performed for Cy3 and Cy5 values, bothfluorescence and background. Once normalized, the background Cy3fluorescence was subtracted from the Cy3 fluorescence for each feature.Values less than 100 were eliminated from further calculations since lowvalues caused spurious results.

FIG. 5 shows the average background subtracted signal for each of nineleukocyte-specific genes on the mini array. This average is for 3-6 ofthe above-described hybridizations for each gene. The error bars are theSEM. FIG. 3: The ratio of Cy3 to Cy5 signal is shown for a number ofgenes. This ratio corrects for variability among hybridizations andallows comparison between experiments done at different times. The ratiois calculated as the Cy3 background subtracted signal divided by the Cy5background subtracted signal. Each bar is the average for 3-6hybridizations. The error bars are SEM.

Together, these results show that we can measure expression levels forgenes that are expressed specifically in sub-populations of leukocytes.These expression measurements were made with only 10 μg of leukocytetotal RNA that was labeled directly by reverse transcription. The signalstrength can be increased by improved labeling techniques that amplifyeither the starting RNA or the signal fluorescence. In addition,scanning techniques with higher sensitivity can be used.

Genes in FIGS. 5 and 6:

GenBank Gene Name Gene Name/Description Accession Number Abbreviation Tcell-specific tyrosine kinase Mrna L10717 TKTCS Interleukin 1 alpha(IL 1) mRNA, complete cds NM_000575 IL1A T-cell surface antigen CD2(T11) mRNA, complete cds M14362 CD2 Interleukin-13 (IL-13) precursorgene, complete cds U31120 IL-13 Thymocyte antigen CD1a mRNA, completecds M28825 CD1a CD6 mRNA for T cell glycoprotein CDS NM_006725 CD6 MHCclass II HLA-DQA1 mRNA, complete cds U77589 HLA-DQA1 Granulocytecolony-stimulating factor M28170 CD19 Homo sapiens CD69 antigenNM_001781 CD69

Example 11 Clinical Study to Identify Diagnostic Gene Sets Useful inDiagnosis and Treatment of Cardiac Allograft Recipients

An observational study was conducted in which a prospective cohort ofcardiac transplant recipients were analyzed for associations betweenclinical events or rejection grades and expression of a leukocytecandidate nucleotide sequence library. Patients were identified at 4cardiac transplantation centers while on the transplant waiting list orduring their routing post-transplant care. All adult cardiac transplantrecipients (new or re-transplants) who received an organ at the studycenter during the study period or within 3 months of the start of thestudy period were eligible. The first year after transplantation is thetime when most acute rejection occurs and it is thus important to studypatients during this period. Patients provided informed consent prior tostudy procedures.

Peripheral blood leukocyte samples were obtained from all patients atthe following time points: prior to transplant surgery (when able), thesame day as routinely scheduled screening biopsies, upon evaluation forsuspected acute rejection (urgent biopsies), on hospitalization for anacute complication of transplantation or immunosuppression, and whenCytomegalovirus (CMV) infection was suspected or confirmed. Samples wereobtained through a standard peripheral vein blood draw or through acatheter placed for patient care (for example, a central venous catheterplaced for endocardial biopsy). When blood was drawn from a intravenousline, care was taken to avoid obtaining heparin with the sample as itcan interfere with downstream reactions involving the RNA. Mononuclearcells were prepared from whole blood samples as described in Example 8.Samples were processed within 2 hours of the blood draw and DNA andserum were saved in addition to RNA. Samples were stored at −80° C. oron dry ice and sent to the site of RNA preparation in a sealed containerwith ample dry ice. RNA was isolated from subject samples as describedin Example 8 and hybridized to a candidate library of differentiallyexpressed leukocyte nucleotide sequences, as further described inExamples 20-22. Methods used for amplification, labeling, hybridizationand scanning are described in Example 23. Analysis of human transplantpatient mononuclear cell RNA hybridized to a microarray andidentification of diagnostic gene sets is shown in Example 24.

From each patient, clinical information was obtained at the followingtime points: prior to transplant surgery (when available), the same dayas routinely scheduled screening biopsies, upon evaluation for suspectedacute rejection (e.g., urgent biopsies), on hospitalization for an acutecomplication of transplantation or immunosuppression, and whenCytomegalovirus (CMV) infection was suspected or confirmed. Data wascollected directly from the patient, from the patient's medical record,from diagnostic test reports or from computerized hospital databases. Itwas important to collect all information pertaining to the studyclinical correlates (diagnoses and patient events and states to whichexpression data is correlated) and confounding variables (diagnoses andpatient events and states that may result in altered leukocyte geneexpression. Examples of clinical data collected are: patient sex, dateof birth, date of transplant, race, requirement for prospective crossmatch, occurrence of pre-transplant diagnoses and complications,indication for transplantation, severity and type of heart disease,history of left ventricular assist devices, all known medical diagnoses,blood type, HLA type, viral serologies (including CMV, Hepatitis B andC, HIV and others), serum chemistries, white and red blood cell countsand differentials, CMV infections (clinical manifestations and methodsof diagnosis), occurrence of new cancer, hemodynamic parameters measuredby catheterization of the right or left heart (measures of graftfunction), results of echocardiography, results of coronary angiograms,results of intravascular ultrasound studies (diagnosis of transplantvasculopathy), medications, changes in medications, treatments forrejection, and medication levels. Information was also collectedregarding the organ donor, including demographics, blood type, HLA type,results of screening cultures, results of viral serologies, primarycause of brain death, the need for inotropic support, and the organ coldischemia time.

Of great importance was the collection of the results of endocardialbiopsy for each of the patients at each visit. Biopsy results were allinterpreted and recorded using the international society for heart andlung transplantation (ISHLT) criteria, described below. Biopsypathological grades were determined by experienced pathologists at eachcenter.

ISHLT Criteria Rejection Grade Finding Severity 0 No lymphocyticinfiltrates None 1A Focal (perivascular or interstitial lymphocyticBorderline infiltrates without necrosis) mild 1B Diffuse but sparselymphocytic infiltrates Mild without necrosis 2 One focus only withaggressive lymphocytic Mild, focal infiltrate and/or myocyte damagemoderate 3A Multifocal aggressive lymphocytic infiltrates Moderateand/or myocardial damage 3B Diffuse inflammatory lymphocytic infiltratesBorderline with necrosis Severe 4 Diffuse aggressive polymorphouslymphocytic Severe infiltrates with edema hemorrhage and vasculitis,with necrosis

Because variability exists in the assignment of ISHLT grades, it wasimportant to have a centralized and blinded reading of the biopsy slidesby a single pathologist. This was arranged for all biopsy slidesassociated with samples in the analysis. Slides were obtained andassigned an encoded number. A single pathologist then read all slidesfrom all centers and assigned an ISHLT grade. Grades from the singlepathologist were then compared to the original grades derived from thepathologists at the study centers. For the purposes of correlationanalysis of leukocyte gene expression to biopsy grades, the centralizedreading information was used in a variety of ways (see Example 24 formore detail). In some analyses, only the original reading was used as anoutcome. In other analyses, the result from the centralized reader wasused as an outcome. In other analyses, the highest of the 2 grades wasused. For example, if the original assigned grade was 0 and thecentralized reader assigned a 1A, then 1A was the grade used as anoutcome. In some analyses, the highest grade was used and then samplesassociated with a Grade 1A reading were excluded from the analysis. Insome analyses, only grades with no disagreement between the 2 readingswere used as outcomes for correlation analysis.

Clinical data was entered and stored in a database. The database wasqueried to identify all patients and patient visits that meet desiredcriteria (for example, patients with >grade II biopsy results, no CMVinfection and time since transplant <12 weeks).

The collected clinical data (disease criteria) is used to define patientor sample groups for correlation of expression data. Patient groups areidentified for comparison, for example, a patient group that possesses auseful or interesting clinical distinction, versus a patient group thatdoes not possess the distinction. Examples of useful and interestingpatient distinctions that can be made on the basis of collected clinicaldata are listed here:

1. Rejection episode of at least moderate histologic grade, whichresults in treatment of the patient with additional corticosteroids,anti-T cell antibodies, or total lymphoid irradiation.

2. Rejection with histologic grade 2 or higher.

3. Rejection with histologic grade <2.

4. The absence of histologic rejection and normal or unchanged allograftfunction (based on hemodynamic measurements from catheterization or onechocardiographic data).

5. The presence of severe allograft dysfunction or worsening allograftdysfunction during the study period (based on hemodynamic measurementsfrom catheterization or on echocardiographic data).

6. Documented CMV infection by culture, histology, or PCR, and at leastone clinical sign or symptom of infection.

7. Specific graft biopsy rejection grades

8. Rejection of mild to moderate histologic severity promptingaugmentation of the patient's chronic immunosuppressive regimen

9. Rejection of mild to moderate severity with allograft dysfunctionprompting plasmaphoresis or a diagnosis of “humoral” rejection

10. Infections other than CMV, esp. Epstein Barr virus (EBV)

11. Lymphoproliferative disorder (also called, post-transplant lymphoma)

12. Transplant vasculopathy diagnosed by increased intimal thickness onintravascular ultrasound (IVUS), angiography, or acute myocardialinfarction.

13. Graft Failure or Retransplantation

14. All cause mortality

15. Grade 1A or higher rejection as defined by the initial biopsyreading.

16. Grade 1B or higher rejection as defined by the initial biopsyreading.

17. Grade 1A or higher rejection as defined by the centralized biopsyreading.

18. Grade 1B or higher rejection as defined by the centralized biopsyreading.

19. Grade 1A or higher rejection as defined by the highest of theinitial and centralized biopsy reading.

20. Grade 1B or higher rejection as defined by the highest of theinitial and centralized biopsy reading.

21. Any rejection >Grade 2 occurring in patient at any time in thepost-transplant course.

Expression profiles of subject samples are examined to discover sets ofnucleotide sequences with differential expression between patientgroups, for example, by methods describes above and below.

Non-limiting examples of patient leukocyte samples to obtain fordiscovery of various diagnostic nucleotide sets are as follows:

-   -   a. Leukocyte set to avoid biopsy or select for biopsy:    -    Samples: Grade 0 vs. Grades 1-4    -   b. Leukocyte set to monitor therapeutic response:    -    Examine successful vs. unsuccessful drug treatment.    -    Samples:    -   Successful: Time 1: rejection, Time 2: drug therapy Time 3: no        rejection    -    Unsuccessful: Time 1: rejection, Time 2: drug therapy; Time        3:rejection    -   c. Leukocyte set to predict subsequent acute rejection.    -    Biopsy may show no rejection, but the patient may develop        rejection shortly thereafter. Look at profiles of patients who        subsequently do and do not develop rejection.    -    Samples:    -    Group 1 (Subsequent rejection): Time 1: Grade 0; Time 2:        Grade>0    -    Group 2 (No subsequent rejection): Time 1: Grade 0; Time 2:        Grade 0    -    Focal rejection may be missed by biopsy. When this occurs the        patient may have a Grade 0, but actually has rejection. These        patients may go on to have damage to the graft etc.    -    Samples:    -    Non-rejectors: no rejection over some period of time Rejectors:        an episode of rejection over same period    -   d. Leukocyte set to diagnose subsequent or current graft        failure:    -    Samples:    -    Echocardiographic or catheterization data to define worsening        function over time and correlate to profiles.    -   e. Leukocyte set to diagnose impending active CMV:    -    Samples:    -    Look at patients who are CMV IgG positive. Compare patients        with subsequent (to a sample) clinical CMV infection verses no        subsequent clinical CMV infection.    -   f. Leukocyte set to diagnose current active CMV:    -    Samples:    -    Analyze patients who are CMV IgG positive. Compare patients        with active current clinical CMV infection vs. no active current        CMV infection.

Upon identification of a nucleotide sequence or set of nucleotidesequences that distinguish patient groups with a high degree ofaccuracy, that nucleotide sequence or set of nucleotide sequences isvalidated, and implemented as a diagnostic test. The use of the testdepends on the patient groups that are used to discover the nucleotideset. For example, if a set of nucleotide sequences is discovered thathave collective expression behavior that reliably distinguishes patientswith no histological rejection or graft dysfunction from all others, adiagnostic is developed that is used to screen patients for the need forbiopsy. Patients identified as having no rejection do not need biopsy,while others are subjected to a biopsy to further define the extent ofdisease. In another example, a diagnostic nucleotide set that determinescontinuing graft rejection associated with myocyte necrosis (>grade I)is used to determine that a patient is not receiving adequate treatmentunder the current treatment regimen. After increased or alteredimmunosuppressive therapy, diagnostic profiling is conducted todetermine whether continuing graft rejection is progressing. In yetanother example, a diagnostic nucleotide set(s) that determine apatient's rejection status and diagnose cytomegalovirus infection isused to balance immunosuppressive and anti-viral therapy.

The methods of this example are also applicable to cardiac xenograftmonitoring.

Example 12 Identification of Diagnostic Nucleotide Sets for Kidney andLiver Allograft Rejection

Diagnostic tests for rejection are identified using patient leukocyteexpression profiles to identify a molecular signature correlated withrejection of a transplanted kidney or liver. Blood, or other leukocytesource, samples are obtained from patients undergoing kidney or liverbiopsy following liver or kidney transplantation, respectively. Suchresults reveal the histological grade, i.e., the state and severity ofallograft rejection. Expression profiles are obtained from the samplesas described above, and the expression profile is correlated with biopsyresults. In the case of kidney rejection, clinical data is collectedcorresponding to urine output, level of creatine clearance, and level ofserum creatine (and other markers of renal function). Clinical datacollected for monitoring liver transplant rejection includes,biochemical characterization of serum markers of liver damage andfunction such as SGOT, SGPT, Alkaline phosphatase, GGT, Bilirubin,Albumin and Prothrombin time.

Leukocyte nucleotide sequence expression profiles are collected andcorrelated with important clinical states and outcomes in renal orhepatic transplantation. Examples of useful clinical correlates aregiven here:

1. Rejection episode of at least moderate histologic grade, whichresults in treatment of the patient with additional corticosteriods,anti-T cell antibodies, or total lymphoid irradiation.

2. The absence of histologic rejection and normal or unchanged allograftfunction (based on tests of renal or liver function listed above).

3. The presence of severe allograft dysfunction or worsening allograftdysfunction during the study period (based on tests of renal and hepaticfunction listed above).

4. Documented CMV infection by culture, histology, or PCR, and at leastone clinical sign or symptom of infection.

5. Specific graft biopsy rejection grades

6. Rejection of mild to moderate histologic severity promptingaugmentation of the patient's chronic immunosuppressive regimen

7. Infections other than CMV, esp. Epstein Barr virus (EBV)

8. Lymphoproliferative disorder (also called, post-transplant lymphoma)

9. Graft Failure or Retransplantation

10. Need for hemodialysis or other renal replacement therapy for renaltransplant patients.

11. Hepatic encephalopathy for liver transplant recipients.

12. All cause mortality

Subsets of the candidate library (or of a previously identifieddiagnostic nucleotide set), are identified, according to the aboveprocedures, that have predictive and/or diagnostic value for kidney orliver allograft rejection.

Example 13 Identification of Diagnostic Nucleotide Sequences Sets forUse in the Diagnosis, Prognosis, Risk Stratification, and Treatment ofAtherosclerosis, Stable Angina Pectoris, and Acute Coronary Syndrome

Prediction of Complications of Atherosclerosis: Angina Pectoris.

Over 50 million in the US have atherosclerotic coronary artery disease(CAD). Almost all adults have some atherosclerosis. The most importantquestion is who will develop complications of atherosclerosis. Patientswith angiographically-confirmed atherosclerosis are enrolled in a study,and followed over time. Leukocyte expression profiles are taken at thebeginning of the study, and routinely thereafter. Some patients developangina and others do not. Expression profiles are correlated withdevelopment of angina, and subsets of the candidate library (or apreviously identified diagnostic nucleotide set) are identified,according to the above procedures, that have predictive and/ordiagnostic value for angina pectoris.

Alternatively, patients are followed by serial angiography. Profiles arecollected at the first angiography, and at a repeat angiography at somefuture time (for example, after 1 year). Expression profiles arecorrelated with progression of disease, measured, for example, bydecrease in vessel lumen diameter. Subsets of the candidate library (ora previously identified diagnostic nucleotide set) are identified,according to the above procedures, that have predictive and/ordiagnostic value for progression of atherosclerosis.

Prediction and/or Diagnosis of Acute Coronary Syndrome

The main cause of death due to coronary atherosclerosis is theoccurrence of acute coronary syndromes: myocardial infarction andunstable angina. Patients with at a very high risk of acute coronarysyndrome (e.g., patients with a history of acute coronary syndrome,patients with atherosclerosis, patients with multiple traditional riskfactors, clotting disorders or lupus) are enrolled in a prospectivestudy. Leukocyte expression profiles are taken at the beginning of thestudy period and patients are monitored for the occurrence of unstableangina and/or myocardial infarction. Standard criteria for theoccurrence of an event are used (serum enzyme elevation, EKG, nuclearimaging or other), and the occurrence of these events can be collectedfrom the patient, the patient's physician, the medical record or medicaldatabase. Expression profiles (taken at the beginning of the study) arecorrelated with the occurrence of an acute event. Subsets of thecandidate library (or a previously identified diagnostic nucleotide set)are identified, according to the above procedures, that have predictivevalue for occurrence of an acute event.

In addition, expression profiles (taken at the time that an acute eventoccurs) are correlated with the occurrence of an acute event. Subsets ofthe candidate library (or a previously identified diagnostic nucleotideset) are identified, according to the above procedures, that havediagnostic value for occurrence of an acute event.

Risk Stratification: Occurrence of Coronary Artery Disease

The established and classic risks for the occurrence of coronary arterydisease and complications of that disease are: cigarette smoking,diabetes, hypertension, hyperlipidemia and a family history of earlyatherosclerosis. Obesity, sedentary lifestyle, syndrome X, cocaine use,chronic hemodialysis and renal disease, radiation exposure, endothelialdysfunction, elevated plasma homocysteine, elevated plasma lipoproteina, and elevated CRP. Infection with CMV and chlamydia infection are lesswell established, controversial or putative risk factors for thedisease. These risk factors can be assessed or measured in a population.

Leukocyte expression profiles are measured in a population possessingrisk factors for the occurrence of coronary artery disease. Expressionprofiles are correlated with the presence of one or more risk factors(that may correlate with future development of disease andcomplications). Subsets of the candidate library (or a previouslyidentified diagnostic nucleotide set) are identified, according to theabove procedures, that have predictive value for the development ofcoronary artery disease.

Additional examples of useful correlation groups in cardiology include:

1. Samples from patients with a high risk factor burden (e.g., smoking,diabetes, high cholesterol, hypertension, family history) versus samplesfrom those same patients at different times with fewer risks, or versussamples from different patients with fewer or different risks.

2. Samples from patients during an episode of unstable angina ormyocardial infarction versus paired samples from those same patientsbefore the episode or after recovery, or from different patients withoutthese diagnoses.

3. Samples from patients (with or without documented atherosclerosis)who subsequently develop clinical manifestations of atherosclerosis suchas stable angina, unstable angina, myocardial infarction, or stroke,versus samples from patients (with or without atherosclerosis) who donot develop these manifestations over the same time period.

4. Samples from patients who subsequently respond to a given medicationor treatment regimen versus samples from those same or differentpatients who subsequently do not respond to a given medication ortreatment regimen.

Example 14 Identification of Diagnostic Nucleotide Sets for Use inDiagnosing and Treating Restenosis

Restenosis is the re-narrowing of a coronary artery after anangioplasty. Patients are identified who are about to, or have recentlyundergone angioplasty. Leukocyte expression profiles are measured beforethe angioplasty, and at 1 day and 1-2 weeks after angioplasty or stentplacement. Patients have a follow-up angiogram at 3 months and/or arefollowed for the occurrence of clinical restenosis, e.g., chest pain dueto re-narrowing of the artery, that is confirmed by angiography.Expression profiles are compared between patients with and withoutrestenosis, and candidate nucleotide profiles are correlated with theoccurrence of restenosis. Subsets of the candidate library (or apreviously identified diagnostic nucleotide set) are identified,according to the above procedures, that have predictive value for thedevelopment of restenosis.

Example 15 Identification of Diagnostic Nucleotide Sets for Use inMonitoring Treatment and/or Progression of Congestive Heart Failure

CHF effects greater than 5 million individuals in the US and theprevalence of this disorder is growing as the population ages. Thedisease is chronic and debilitating. Medical expenditures are huge dueto the costs of drug treatments, echocardiograms and other tests,frequent hospitalization and cardiac transplantation. The primary causesof CHF are coronary artery disease, hypertension and idiopathiccardiomyopathy. Congestive heart failure is the number one indicationfor heart transplantation.

There is ample recent evidence that congestive heart failure isassociated with systemic inflammation. A leukocyte test with the abilityto determine the rate of progression and the adequacy of therapy is ofgreat interest. Patients with severe CHF are identified, e.g. in a CHFclinic, an inpatient service, or a CHF study or registry (such as thecardiac transplant waiting list/registry). Expression profiles are takenat the beginning of the study and patients are followed over time, forexample, over the course of one year, with serial assessments performedat least every three months. Further profiles are taken at clinicallyrelevant end-points, for example: hospitalization for CHF, death,pulmonary edema, worsening of Ejection Fraction or increased cardiacchamber dimensions determined by echocardiography or another imagingtest, and/or exercise testing of hemodynamic measurements. Clinical datais collected from patients if available, including:

Serial C-Reactive Protein (CRP), other serum markers, echocardiography(e.g., ejection fraction or another echocardiographic measure of cardiacfunction), nuclear imaging, NYHA functional classes, hospitalizationsfor CHF, quality of life measures, renal function, transplant listing,pulmonary edema, left ventricular assist device use, medication use andchanges.

Expression profiles correlating with progression of CHF are identified.Expression profiles predicting disease progression, monitoring diseaseprogression and response to treatment, and predicting response to aparticular treatment(s) or class of treatment(s) are identified. Subsetsof the candidate library (or a previously identified diagnosticnucleotide set) are identified, according to the above procedures, thathave predictive value for the progression of CHF. Such diagnosticnucleotide sets are also useful for monitoring response to treatment forCHF.

Example 16 Identification of Diagnostic Nucleotide Sets for Use inMonitoring Treatment and/or Progression of Rheumatoid Arthritis

Rheumatoid arthritis (hereinafter, “RA”) is a chronic and debilitatinginflammatory arthritis. The diagnosis of RA is made by clinical criteriaand radiographs. A new class of medication, TNF blockers, are effective,but the drugs are expensive, have side effects and not all patientsrespond to treatment. In addition, relief of disease symptoms does notalways correlate with inhibition of joint destruction. For thesereasons, an alternative mechanism for the titration of therapy isneeded.

An observational study was conducted in which a cohort of patientsmeeting American College of Rheumatology (hereinafter “ARC”) criteriafor the diagnosis of RA was identified. Arnett et al. (1988) ArthritisRheum 31:315-24. Patients gave informed consent and a peripheral bloodmononuclear cell RNA sample was obtained by the methods as describedherein. When available, RNA samples were also obtained from surgicalspecimens of bone or synovium from effected joints, and synovial fluid.

From each patient, the following clinical information was obtained ifavailable:

Demographic information; information relating to the ACR criteria forRA; presence or absence of additional diagnoses of inflammatory andnon-inflammatory conditions; data from laboratory test, includingcomplete blood counts with differentials, CRP, ESR, ANA, Serum IL6,Soluble CD40 ligand, LDL, HDL, Anti-DNA antibodies, rheumatoid factor,C3, C4, serum creatinine and any medication levels; data from surgicalprocedures such as gross operative findings and pathological evaluationof resected tissues and biopsies; information on pharmacological therapyand treatment changes; clinical diagnoses of disease “flare”;hospitalizations; quantitative joint exams; results from healthassessment questionnaires (HAQs); other clinical measures of patientsymptoms and disability; physical examination results and radiographicdata assessing joint involvement, synovial thickening, bone loss anderosion and joint space narrowing and deformity.

From these data, measures of improvement in RA are derived asexemplified by the ACR 20% and 50% response/improvement rates (Felson etal. 1996). Measures of disease activity over some period of time isderived from these data as are measures of disease progression. Serialradiography of effected joints is used for objective determination ofprogression (e.g., joint space narrowing, peri-articular osteoporosis,synovial thickening). Disease activity is determined from the clinicalscores, medical history, physical exam, lab studies, surgical andpathological findings.

The collected clinical data (disease criteria) is used to define patientor sample groups for correlation of expression data. Patient groups areidentified for comparison, for example, a patient group that possesses auseful or interesting clinical distinction, verses a patient group thatdoes not possess the distinction. Examples of useful and interestingpatient distinctions that can be made on the basis of collected clinicaldata are listed here:

1. Samples from patients during a clinically diagnosed RA flare versussamples from these same or different patients while they areasymptomatic.

2. Samples from patients who subsequently have high measures of diseaseactivity versus samples from those same or different patients who havelow subsequent disease activity.

3. Samples from patients who subsequently have high measures of diseaseprogression versus samples from those same or different patients whohave low subsequent disease progression.

4. Samples from patients who subsequently respond to a given medicationor treatment regimen versus samples from those same or differentpatients who subsequently do not respond to a given medication ortreatment regimen (for example, TNF pathway blocking medications).

5. Samples from patients with a diagnosis of osteoarthritis versuspatients with rheumatoid arthritis.

6. Samples from patients with tissue biopsy results showing a highdegree of inflammation versus samples from patients with lesser degreesof histological evidence of inflammation on biopsy.

Expression profiles correlating with progression of RA are identified.Subsets of the candidate library (or a previously identified diagnosticnucleotide set) are identified, according to the above procedures, thathave predictive value for the progression of RA.

Diagnostic nucleotide set(s) are identified which predict respond to TNFblockade. Patients are profiled before and during treatment with thesemedications. Patients are followed for relief of symptoms, side effectsand progression of joint destruction, e.g., as measured by handradiographs. Expression profiles correlating with response to TNFblockade are identified. Subsets of the candidate library (or apreviously identified diagnostic nucleotide set) are identified,according to the above procedures that have predictive value forresponse to TNF blockade.

Example 17 Identification of a Diagnostic Nucleotide Set for Diagnosisof Cytomegalovirus

Cytomegalovirus is a very important cause of disease inimmunocompromised patients, for example, transplant patients, cancerpatients, and AIDS patients. The virus can cause inflammation anddisease in almost any tissue (particularly the colon, lung, bone marrowand retina). It is increasingly important to identify patients withcurrent or impending clinical CMV disease, particularly whenimmunosuppressive drugs are to be used in a patient, e.g. for preventingtransplant rejection.

Leukocytes are profiled in patients with active CMV, impending CMV, orno CMV. Expression profiles correlating with diagnosis of active orimpending CMV are identified. Subsets of the candidate library (or apreviously identified diagnostic nucleotide set) are identified,according to the above procedures that have predictive value for thediagnosis of active or impending CMV. Diagnostic nucleotide set(s)identified with predictive value for the diagnosis of active orimpending CMV may be combined, or used in conjunction with, cardiac,liver and/or kidney allograft-related diagnostic gene set(s) (describedin Examples 12 and 24).

In addition, or alternatively, CMV nucleotide sequences are obtained,and a diagnostic nucleotide set is designed using CMV nucleotidesequence. The entire sequence of the organism is known and all CMVnucleotide sequences can be isolated and added to the library using thesequence information and the approach described below. Known expressedgenes are preferred. Alternatively, nucleotide sequences are selected torepresent groups of CMV genes that are coordinately expressed (immediateearly genes, early genes, and late genes) (Spector et al. 1990,Stamminger et al. 1990).

Oligonucleotides were designed for CMV genes using the oligo designprocedures of Example 21. Probes were designed using the 14 genesequences shown here and were included on the array described inexamples 20-22:

Cytomegalovirus HCMVTRL2 (IRL2) 1893 . . . 2240 (CMV) HCMVTRL7 (IRL7)complement(6595 . . . 6843) Accession #X17403 HCMVUL21 complement(26497. . . 27024) HCMVUL27 complement(32831 . . . 34657) HCMVUL33 43251 . . .44423 HCMVUL54 complement(76903 . . . 80631) HCMVUL75 complement(107901. . . 110132) HCMVUL83 complement(119352 . . . 121037) HCMVUL106complement(154947 . . . 155324) HCMVUL109 complement(157514 . . .157810) HCMVUL113 161503 . . . 162800 HCMVUL122 complement(169364 . . .170599) HCMVUL123 (last exon at 3′-end) complement(171006 . . . 172225)HCMVUS28 219200 . . . 220171

Diagnostic nucleotide set(s) for expression of CMV genes is used incombination with diagnostic leukocyte nucleotide sets for diagnosis ofother conditions, e.g. organ allograft rejection.

Using the techniques described in example 8 mononuclear samples from 180cardiac transplant recipients (enrolled in the study described inExample 11) were used for expression profiling with the leukocytearrays. Of these samples 15 were associated with patients who had adiagnosis of primary or reactivation CMV made by culture, PCR or anyspecific diagnostic test.

After preparation of RNA, amplification, labeling, hybridization,scanning, feature extraction and data processing were done as describedin Example 23 using the oligonucleotide microarrays described inExamples 20-22.

The resulting log ratio of expression of Cy3 (patient sample)/Cy5 (R50reference RNA) was used for analysis. Significance analysis formicroarrays (SAM, Tusher 2001, see Example 26) was applied to determinewhich genes were most significantly differentially expressed betweenthese 15 CMV patients and the 165 non-CMV patients (Table 11A). 12 geneswere identified with a 0% FDR and 6 with a 0.1% FDR. Some genes arerepresented by more than one oligonucleotide on the array and for 2genes, multiple oligonucleotides from the same gene are calledsignificant (SEQ IDs: 5559, 6308: eomesodermin and 1685, 2428, 4113,6059: small inducible cytokine A4).

Clinical variables were also included in the significance analysis. Forexample, the white blood cell count and the number of weeks posttransplant (for the patient at the time the sample was obtained) wereavailable for most of the 180 samples. The log of these variables wastaken and the variables were then used in the significance analysisdescribed above with the gene expression data. Both the white blood cellcount (0.1% FDR) and the weeks post transplant (0% FDR) appeared tocorrelate with CMV status. CMV patients were more likely to have samplesassociated with later post transplant data and the lower white bloodcell counts.

These genes and variables can be used alone or in association with othergenes or variables or with other genes to build a diagnostic gene set ora classification algorithm using the approaches described herein.

Primers for real-time PCR validation were designed for some of thesegenes as described in Example 25 and listed in Table 11B and thesequence listing. Using the methods described in example 25, primers forGranzyme B were designed and used to validate expression findings fromthe arrays. 6 samples were tested (3 from patients with CMV and 3 frompatients without CMV). The gene was found to be differentially expressedbetween the patients with and without CMV (see example 25 for fulldescription). This same approach can be used to validate otherdiagnostic genes by real-time PCR.

Diagnostic nucleotide sets can also be identified for a variety of otherviral diseases (Table 1) using this same approach.

Example 18 Identification of a Diagnostic Nucleotide Set for Diagnosisof Cytomegalovirus

Cytomegalovirus is a very important cause of disease in immunosupressedpatients, for example, transplant patients, cancer patients, and AIDSpatients. The virus can cause inflammation and disease in almost anytissue (particularly the colon, lung, bone marrow and retina). It isincreasingly important to identify patients with current or impendingclinical CMV disease, particularly when immunosuppressive drugs are tobe used in a patient, e.g. for preventing transplant rejection.

Leukocytes are profiled in patients with active CMV, impending CMV, orno CMV. Expression profiles correlating with diagnosis of active orimpending CMV are identified. Subsets of the candidate library (or apreviously identified diagnostic nucleotide set) are identified,according to the above procedures, that have predictive value for thediagnosis of active or impending CMV. Diagnostic nucleotide set(s)identified with predictive value for the diagnosis of active orimpending CMV may be combined, or used in conjunction with, cardiac,liver and/or kidney allograft-related diagnostic gene set(s) (describedin Examples 11 and 12).

In addition, or alternatively, CMV nucleotide sequences are obtained,and a diagnostic nucleotide set is designed using CMV nucleotidesequence. The entire sequence of the organism is known and all CMVnucleotide sequences can be isolated and added to the library using thesequence information and the approach described below. Known expressedgenes are preferred. Alternatively, nucleotide sequences are selected torepresent groups of CMV genes that are coordinately expressed (immediateearly genes, early genes, and late genes) (Spector et al. 1990,Stamminger et al. 1990).

CMV nucleotide sequences were isolated as follows: Primers were designedto amplify known expressed CMV genes, based on the publically availablesequence of CMV strain AD 169 (Genbank LOCUS: HEHCMVCG 229354 bp;DEFINITION Human cytomegalovirus strain AD169 complete genome; ACCESSIONX17403; VERSION X17403.1 GI:59591). The following primer were used toPCR amplify nucleotide sequences from 175 ng of AD 169 viral genomic DNA(Advance Biotechnologies Incorporated) as a template:

CMV GENE PRIMER SEQUENCES SEQ. ID. NO: UL21 5′ atgtggccgcttctgaaaaac8771 UL21 3′ tcatggggtggggacgggg 8772 UL33 5′ gtacgcgctgctgggtcatg 8773UL33 3′ tcataccccgctgaggttatg 8774 UL54 5′ cacggacgacgacgctgacg 8775UL54 3′ gtacggcagaaaagccggctc 8776 UL55 5′ caccaaagacacgtcgttacag 8777UL55 3′ tcagacgttctcttcttcgtcg 8778 UL75 5′ cagcggcgctcaacatttcac 8779UL75 3′ tcagcatgtcttgagcatgcgg 8780 UL80 5′ cctccccaactactactaccg 8781UL80 3′ ttactcgagcttattgagcgcag 8782 UL83 5′ cacgtcgggcgttatgacac 8783UL83 3′ tcaacctcggtgctttttggg 8784 UL97 5′ ctgtctgctcattctggcgg 8785UL97 3′ ttactcggggaacagttggcg 8786 UL106 5′ atgatgaccgaccgcacgga 8787UL106 3′ tcacggtggctcgatacactg 8788 UL107 5′ aagcttccttacagcataactgt8789 UL107 3′ ccttataacatgtattttgaaaaattg 8790 UL109 5′atgatacacgactaccactgg 8791 UL109 3′ ttacgagcaagagttcatcacg 8792 UL112 5′ctgcgtgtcctcgctgggt 8793 UL112 3′ tcacgagtccactcggaaagc 8794 UL113 5′ctcgtcttcttcggctccac 8795 UL113 3′ ttaatcgtcgaaaaacgccgcg 8796 UL122 5′gatgcttgtaacgaaggcgtc 8797 UL122 3′ ttactgagacttgttcctcagg 8798 UL123 5′gtagcctacactttggccacc 8799 UL123 3′ ttactggtcagccttgcttcta 8800 IRL2 5′acgtccctggtagacggg 8801 IRL2 3′ ttataagaaaagaagcacaagctc 8802 IRL3 5′atgtattgttttctttttttacagaaag 8803 IRL3 3′ ttatattattatcaaaacgaaaaacag8804 IRL4 5′ cttctcctttccttaatctcgg 8805 IRL4 3′ ctatacggagatcgcggtcc8806 IRL5 5′ atgcatacatacacgcgtgcat 8807 IRL5 3′ ctaccatataaaaacgcagggg8808 IRL7 5′ atgaaagcaagaggcagccg 8809 IRL7 3′ tcataaggtaacgatgctacttt8810 IRL13 5′ atggactggcgatttacggtt 8811 IRL13 3′ ctacattgtgccatttctcagt8812 US2 5′ atgaacaatctctggaaagcctg 8813 US2 3′ tcagcacacgaaaaaccgcatc8814 US3 5′ atgaagccggtgttggtgctc 8815 US3 3′ ttaaataaatcgcagacgggccg8816 US6 5′ atggatctcttgattcgtctcg 8817 US6 3′ tcaggagccacaacgtcgaatc8818 US11 5′ cgcaaaacgctactggctcc 8819 US11 3′ tcaccactggtccgaaaacatc8820 US18 5′ tacggctggtccgtcatcgt 8821 US18 3′ ttacaacaagctgaggagactc8822 US27 5′ atgaccacctctacaaataatcaaac 8823 US27 5′gtagaaacaagcgttgagtccc 8824 US28 5′ cgttgcggtgtctcagtcg 8825 US28 3′tcatgctgtggtaccaggata 8826

The PCR reaction conditions were 10 mM Tris pH 8.3, 3.5 mM MgCl2, 25 mMKCl, 200 uM dNTP's, 0.2 uM primers, and 5 Units of Taq Gold. The cycleparameters were as follows:

1. 95° C. for 30 sec

2. 95° C. for 15 sec

3. 56° C. for 30 sec

4. 72° C. for 2 min

5. go to step 2, 29 times

6. 72° C. for 2 min

7. 4° C. forever

PCR products were gel purified, and DNA was extracted from the agaroseusing the Qiaexll gel purification kit (Qiagen). PCR product was ligatedinto the T/A cloning vector p-GEM-T-Easy (Promega) using 3 ul of gelpurified PCR product and following the Promega protocol. The products ofthe ligation reaction were transformed and plated as described in thep-GEM protocol. White colonies were picked and grow culture in LB-AMPmedium. Plasmid was prepared from these cultures using Qiagen Miniprepkit (Qiagen). Restriction enzyme digested plasmid (Not I and EcoRI) wasexamined after agarose gel electrophoresis to assess insert size. Whenthe insert was the predicted size, the plasmid was sequenced bywell-known techniques to confirm the identity of the CMV gene. Usingforward and reverse primers that are complimentary to sequences flankingthe insert cloning site (M13F and M13R), the isolated CMV gene wasamplified and purified as described above. Amplified cDNAs were used tocreate a microarray as described above. In addition, 50meroligonucleotides corresponding the CMV genes listed above were designed,synthesized and placed on a microarray using methods described elsewherein the specification.

Alternatively, oligonucleotide sequences aredesigned and synthesized foroligonucleotide array expression analysis from CMV genes as described inexamples 20-22.

Diagnostic nucleotide set(s) for expression of CMV genes is used incombination with diagnostic leukocyte nucleotide sets for diagnosis ofother conditions, e.g. organ allograft rejection.

Example 19 Identification of Diagnostic Nucleotide Sets for MonitoringResponse to Statins

HMG-CoA reductase inhibitors, called “Statins,” are very effective inpreventing complications of coronary artery disease in either patientswith coronary disease and high cholesterol (secondary prevention) orpatients without known coronary disease and with high cholesterol(primary prevention). Examples of Statins are (generic names given)pravistatin, atorvastatin, and simvastain. Monitoring response to Statintherapy is of interest. Patients are identified who are on or are aboutto start Statin therapy. Leukocytes are profiled in patients before andafter initiation of therapy, or in patients already being treated withStatins. Data is collected corresponding to cholesterol level, markersof inflammation (e.g., C-Reactive Protein and the ErythrocyteSedimentation Rate), measures of endothelial function (e.g., improvedforearm resistance or coronary flow reserve) and clinical endpoints (newstable angina, unstable angina, myocardial infarction, ventriculararrhythmia, claudication). Patient groups can be defined based on theirresponse to Statin therapy (cholesterol, clinical endpoints, endothelialfunction). Expression profiles correlating with response to Statintreatment are identified. Subsets of the candidate library (or apreviously identified diagnostic nucleotide set) are identified,according to the above procedures, that have predictive value for theresponse to Statins. Members of candidate nucleotide sets withexpression that is altered by Statins are disease target nucleotidessequences.

Example 20 Probe Selection for a 24,000 Feature Array

This Example describes the compilation of almost 8,000 unique genes andESTs using sequences identified from the sources described below. Thesequences of these genes and ESTs were used to design probes, asdescribed in the following Example.

Tables 3A, 3B and 3C list the sequences identified in the subtractedleukocyte expression libraries. All sequences that were identified ascorresponding to a known RNA transcript were represented at least once,and all unidentified sequences were represented twice—once by thesequence on file and again by the complementary sequence—to ensure thatthe sense (or coding) strand of the gene sequence was included.

Table 3A. Table 3A contained all those sequences in the subtractedlibraries of example 1 that matched sequences in GenBank's nr,EST_Human, and UniGene databases with an acceptable level of confidence.All the entries in the table representing the sense strand of theirgenes were grouped together and all those representing the antisensestrand were grouped. A third group contained those entries whose strandcould not be determined. Two complementary probes were designed for eachmember of this third group.

Table 3B and 3C. Table 3B and 3C contained all those sequences in theleukocyte expression subtracted libraries of example 1 that did notmatch sequences in GenBank's nr, EST_Human, and UniGene databases withan acceptable level of confidence, but which had a high probability ofrepresenting real mRNA sequences. Sequences in Table 3B did not matchanything in the databases above but matched regions of the human genomedraft and were spatially clustered along it, suggesting that they wereexons, rather than genomic DNA included in the library by chance.Sequences in Table 3C also aligned well to regions of the human genomedraft, but the aligned regions were interrupted by genomic DNA, meaningthey were likely to be spliced transcripts of multiple exon genes.

Table 3B lists 510 clones and Table 3C lists 48 clones that originallyhad no similarity with any sequence in the public databases. Blastnsearches conducted after the initial filing have identified sequences inthe public database with high similarity (E values less than 1e-40) tothe sequences determined for these clones. Table 3B contained 272 clonesand Table 3C contained 25 clones that were found to have high similarityto sequences in dbEST. The sequences of the similar dbEST clones wereused to design probes. Sequences from clones that contained no similarregions to any sequence in the database were used to design a pair ofcomplementary probes.

Probes were designed from database sequences that had the highestsimilarity to each of the sequenced clones in Tables 3A, 3B, and 3C.Based on BLASTn searches the most similar database sequence wasidentified by locus number and the locus number was submitted to GenBankusing batch Entrez located at the web sitencbi.nlm.nih.gov/entrez/batchentrez.cgi?db=Nucleotide to obtain thesequence for that locus. The GenBank entry sequence was used because inmost cases it was more complete or was derived from multi-passsequencing and thus would likely have fewer errors than the single passcDNA library sequences. When only UniGene cluster IDs were available forgenes of interest, the respective sequences were extracted from theUniGene unique database, build 137, downloaded from NCBI(ftp://ncbi.nlm.nih.gov/repository/UniGene/). This database contains onerepresentative sequence for each cluster in UniGene.

Summary of library clones used in array probe design Table Sense StrandAntisnese Strand Strand Undetermined 3A 3621 763 124 3B 142 130 238 3C19 6 23 Totals 3782 899 385

Literature Searches

Example 2 describes searches of literature databases. We also searchedfor research articles discussing genes expressed only in leukocytes orinvolved in inflammation and particular disease conditions, includinggenes that were specifically expressed or down-regulated in a diseasestate. Searches included, but were not limited to, the following termsand various combinations of theses terms: inflammation, atherosclerosis,rheumatoid arthritis, osteoarthritis, lupus, SLE, allograft, transplant,rejection, leukocyte, monocyte, lymphocyte, mononuclear, macrophage,neutrophil, eosinophil, basophil, platelet, congestive heart failure,expression, profiling, microarray, inflammatory bowel disease, asthma,RNA expression, gene expression, granulocyte.

A UniGene cluster ID or GenBank accession number was found for each genein the list. The strand of the corresponding sequence was determined, ifpossible, and the genes were divided into the three groups: sense(coding) strand, anti-sense strand, or strand unknown. The rest of theprobe design process was carried out as described above for thesequences from the leukocyte subtracted expression library.

Database Mining

Database mining was performed as described in Example 2. In addition,the Library Browser at the NCBI UniGene web sitencbi.nlm.nih.gov/UniGene/lbrowse.cgi?ORG=Hs&DISPLAY=ALL was used toidentify genes that are specifically expressed in leukocyte cellpopulations. All expression libraries available at the time wereexamined and those derived from leukocytes were viewed individually.Each library viewed through the Library Browser at the UniGene web sitecontains a section titled “Shown below are UniGene clusters of specialinterest only” that lists genes that are either highly represented orfound only in that library. Only the genes in this section weredownloaded from each library. Alternatively, every sequence in eachlibrary is downloaded and then redundancy between libraries is reducedby discarding all UniGene cluster IDs that are represented more thanonce. A total of 439 libraries were downloaded, containing 35,819 genes,although many were found in more than one library. The most importantlibraries from the remaining set were separated and 3,914 genesremained. After eliminating all redundancy between these libraries andcomparing the remaining genes to those listed in Tables 3A, 3B and 3C,the set was reduced to 2,573 genes in 35 libraries as shown in Table 9.From these, all genes in first 30 libraries were used to design probes.A random subset of genes was used from Library Lib.376, “ActivatedT-cells XX”. From the last four libraries, a random subset of sequenceslisted as “ESTs, found only in this library” was used.

Angiogenesis Markers

215 sequences derived from an angiogenic endothelial cell subtractedcDNA library obtained from Stanford University were used for probedesign. Briefly, using well known subtractive hybridization procedures,(as described in, e.g., U.S. Pat. Nos. 5,958,738; 5,589,339; 5,827,658;5,712,127; 5,643,761; 5,565,340) modified to normalize expression bysuppressing over-representation of abundant RNA species while increasingrepresentation of rare RNA species, a library was produced that isenriched for RNA species (messages) that are differentially expressedbetween test (stimulated) and control (resting) HUVEC populations. Thesubtraction/suppression protocol was performed as described by the kitmanufacturer (Clontech, PCR-select cDNA Subtraction Kit).

Pooled primary HUVECs (Clonetics) were cultured in 15% FCS, M199(GibcoBRL) with standard concentrations of Heparin, Penicillin,Streptomycin, Glutamine and Endothelial Cell Growth Supplement. Thecells were cultured on 1% gelatin coated 10 cm dishes. Confluent HUVECswere photographed under phase contrast microscopy. The cells formed amonolayer of flat cells without gaps. Passage 2-5 cells were used forall experiments. Confluent HUVECs were treated with trypsin/EDTA andseeded onto collagen gels. Collagen gels were made according to theprotocol of the Collagen manufacturer (Becton Dickinson Labware).Collagen gels were prepared with the following ingredients: Rat tailcollagen type I (Collaborative Biomedical) 1.5 mg/mL, mouse laminin(Collaborative Biomedical) 0.5 mg/mL, 10% 10× media 199 (Gibco BRL). 1NNaOH, 10×PBS and sterile water were added in amounts recommended in theprotocol. Cell density was measured by microscopy. 1.2×10⁶ cells wereseeded onto gels in 6-well, 35 mm dishes, in 5% FCS M199 media. Thecells were incubated for 2 hrs at 37 C with 5% CO2. The media was thenchanged to the same media with the addition of VEGF (Sigma) at 30 ng/mLmedia. Cells were cultured for 36 hrs. At 12, 24 and 36 hrs, the cellswere observed with phase contrast microscopy. At 36 hours, the cellswere observed elongating, adhering to each other and forming lumenstructures. At 12 and 24 hrs media was aspirated and refreshed. At 36hrs, the media was aspirated, the cells were rinsed with PBS and thentreated with Collagenase (Sigma) 2.5 mg/mL PBS for 5 min with activeagitation until the collagen gels were liquefied. The cells were thencentrifuged at 4C, 2000 g for 10 min. The supernatant was removed andthe cells were lysed with 1 mL Trizol Reagent (Gibco) per 5×10⁶ cells.Total RNA was prepared as specified in the Trizol instructions for use.mRNA was then isolated as described in the micro-fast track mRNAisolation protocol from Invitrogen. This RNA was used as the tester RNAfor the subtraction procedure.

Ten plates of resting, confluent, p4 HUVECs, were cultured with 15% FCSin the M199 media described above. The media was aspirated and the cellswere lysed with 1 mL Trizol and total RNA was prepared according to theTrizol protocol. mRNA was then isolated according to the micro-fasttrack mRNA isolation protocol from Invitrogen. This RNA served as thecontrol RNA for the subtraction procedure.

The entire subtraction cloning procedure was carried out as per the usermanual for the Clontech PCR Select Subtraction Kit. The cDNAs preparedfrom the test population of HUVECs were divided into “tester” pools,while cDNAs prepared from the control population of HUVECs weredesignated the “driver” pool. cDNA was synthesized from the tester andcontrol RNA samples described above. Resulting cDNAs were digested withthe restriction enzyme RsaI. Unique double-stranded adapters wereligated to the tester cDNA. An initial hybridization was performedconsisting of the tester pools of cDNA (with its corresponding adapter)and an excess of the driver cDNA. The initial hybridization results in apartial normalization of the cDNAs such that high and low abundancemessages become more equally represented following hybridization due toa failure of driver/tester hybrids to amplify.

A second hybridization involved pooling unhybridized sequences from thefirst hybridization together with the addition of supplemental drivercDNA. In this step, the expressed sequences enriched in the two testerpools following the initial hybridization can hybridize. Hybridsresulting from the hybridization between members of each of the twotester pools are then recovered by amplification in a polymerase chainreaction (PCR) using primers specific for the unique adapters. Again,sequences originating in a tester pool that form hybrids with componentsof the driver pool are not amplified. Hybrids resulting between membersof the same tester pool are eliminated by the formation of “panhandles”between their common 5′ and 3′ ends. The subtraction was done in bothdirections, producing two libraries, one with clones that areupregulated in tube-formation and one with clones that aredown-regulated in the process.

The resulting PCR products representing partial cDNAs of differentiallyexpressed genes were then cloned (i.e., ligated) into an appropriatevector according to the manufacturer's protocol (pGEM-Teasy fromPromega) and transformed into competent bacteria for selection andscreening. Colonies (2180) were picked and cultured in LB broth with 50ug/mL ampicillin at 37 C overnight. Stocks of saturated LB +50 ug/mLampicillin and 15% glycerol in 96-well plates were stored at −80 C.Plasmid was prepared from 1.4 mL saturated LB broth containing 50 ug/mLampicillin. This was done in a 96 well format using commerciallyavailable kits according to the manufacturer's recommendations (Qiagen96-turbo prep).

2 probes to represent 22 of these sequences required, therefore, a totalof 237 probes were derived from this library.

Viral Genes

Several viruses may play a role in a host of disease includinginflammatory disorders, atherosclerosis, and transplant rejection. Table10 lists the viral genes represented by oligonucleotide probes on themicroarray. Low-complexity regions in the sequences were masked usingRepeatMasker before using them to design probes.

Strand Selection

It was necessary to design sense oligonucleotide probes because thelabeling and hybridization protocol to be used with the microarrayresults in fluorescently-labeled antisense cRNA. All of the sequences weselected to design probes could be divided into three categories:

(1) Sequences known to represent the sense strand

(2) Sequences known to represent the antisense strand

(3) Sequences whose strand could not be easily determined from theirdescriptions

It was not known whether the sequences from the leukocyte subtractedexpression library were from the sense or antisense strand. GenBanksequences are reported with sequence given 5′ to 3′, and the majority ofthe sequences we used to design probes came from accession numbers withdescriptions that made it clear whether they represented sense orantisense sequence. For example, all sequences containing “mRNA” intheir descriptions were understood to be the sequences of the sensemRNA, unless otherwise noted in the description, and all IMAGEConsortium clones are directionally cloned and so the direction (orsense) of the reported sequence can be determined from the annotation inthe GenBank record.

For accession numbers representing the sense strand, the sequence wasdownloaded and masked and a probe was designed directly from thesequence. These probes were selected as close to the 3′ end as possible.For accession numbers representing the antisense strand, the sequencewas downloaded and masked, and a probe was designed complementary tothis sequence. These probes were designed as close to the 5′ end aspossible (i.e., complementary to the 3′ end of the sense strand).

Minimizing Probe Redundancy.

Multiple copies of certain genes or segments of genes were included inthe sequences from each category described above, either by accident orby design. Reducing redundancy within each of the gene sets wasnecessary to maximize the number of unique genes and ESTs that could berepresented on the microarray.

Three methods were used to reduce redundancy of genes, depending on whatinformation was available. First, in gene sets with multiple occurrencesof one or more UniGene numbers, only one occurrence of each UniGenenumber was kept. Next, each gene set was searched by GenBank accessionnumbers and only one occurrence of each accession number was conserved.Finally, the gene name, description, or gene symbol were searched forredundant genes with no UniGene number or different accession numbers.In reducing the redundancy of the gene sets, every effort was made toconserve the most information about each gene.

We note, however, that the UniGene system for clustering submissions toGenBank is frequently updated and UniGene cluster IDs can change. Two ormore clusters may be combined under a new cluster ID or a cluster may besplit into several new clusters and the original cluster ID retired.Since the lists of genes in each of the gene sets discussed wereassembled at different times, the same sequence may appear in severaldifferent sets with a different UniGene ID in each.

Sequences from Table 3A were treated differently. In some cases, two ormore of the leukocyte subtracted expression library sequences aligned todifferent regions of the same GenBank entry, indicating that thesesequences were likely to be from different exons in the same genetranscript. In these cases, one representative library sequencecorresponding to each presumptive exon was individually listed in Table3A.

Compilation.

After redundancy within a gene set was sufficiently reduced, a table ofapproximately 8,000 unique genes and ESTs was compiled in the followingmanner. All of the entries in Table 3A were transferred to the newtable. The list of genes produced by literature and database searcheswas added, eliminating any genes already contained in Table 3A. Next,each of the remaining sets of genes was compared to the table and anygenes already contained in the table were deleted from the gene setsbefore appending them to the table.

Probes Subtracted Leukocyte Expression Library Table 3A 4,872 Table 3B796 Table 3C 85 Literature Search Results 494 Database Mining 1,607Viral genes a. CMV 14 b. EBV 6 c. HHV 6 14 d. Adenovirus 8 Angiogenesismarkers: 215, 22 of 237 which needed two probes Arabidopsis thalianagenes 10 Total sequences used to design probes 8,143

Example 21 Design of Oligonucleotide Probes

By way of example, this section describes the design of fouroligonucleotide probes using Array Designer Ver 1.1 (Premier BiosoftInternational, Palo Alto, Calif.). The major steps in the process aregiven first.

-   1. Obtain best possible sequence of mRNA from GenBank. If a    full-length sequence reference sequence is not available, a partial    sequence is used, with preference for the 3′ end over the 5′ end.    When the sequence is known to represent the antisense strand, the    reverse complement of the sequence is used for probe design. For    sequences represented in the subtracted leukocyte expression library    that have no significant match in GenBank at the time of probe    design, our sequence is used.-   2. Mask low complexity regions and repetitive elements in the    sequence using an algorithm such as RepeatMasker.-   3. Use probe design software, such as Array Designer, version 1.1,    to select a sequence of 50 residues with specified physical and    chemical properties. The 50 residues nearest the 3′ end constitute a    search frame. The residues it contains are tested for suitability.    If they don't meet the specified criteria, the search frame is moved    one residue closer to the 5′ end, and the 50 residues it now    contains are tested. The process is repeated until a suitable 50-mer    is found.-   4. If no such 50-mer occurs in the sequence, the physical and    chemical criteria are adjusted until a suitable 50-mer is found.

Compare the probe to dbEST, the UniGene cluster set, and the assembledhuman genome using the BLASTn search tool at NCBI to obtain thepertinent identifying information and to verify that the probe does nothave significant similarity to more than one known gene.

Clone 40H12

Clone 40H12 was sequenced and compared to the nr, dbEST, and UniGenedatabases at NCBI using the BLAST search tool. The sequence matchedaccession number NM_(—)002310, a ‘curated RefSeq project’ sequence, seePruitt et al. (2000) Trends Genet. 16:44-47, encoding leukemiainhibitory factor receptor (LIFR) mRNA with a reported E value of zero.An E value of zero indicates there is, for all practical purposes, nochance that the similarity was random based on the length of thesequence and the composition and size of the database. This sequence,cataloged by accession number NM_(—)002310, is much longer than thesequence of clone 40H12 and has a poly-A tail. This indicated that thesequence cataloged by accession number NM_(—)002310 is the sense strandand a more complete representation of the mRNA than the sequence ofclone 40H12, especially at the 3′ end. Accession number “NM_(—)002310”was included in a text file of accession numbers representing sensestrand mRNAs, and sequences for the sense strand mRNAs were obtained byuploading a text file containing desired accession numbers as an Entrezsearch query using the Batch Entrez web interface and saving the resultslocally as a FASTA file. The following sequence was obtained, and theregion of alignment of clone 40H12 is outlined:

(SEQ ID NO: 8827)CTCTCTCCCAGAACGTGTCTCTGCTGCAAGGCACCGGGCCCTTTCGCTCTGCAGAACTGCACTTGCAAGACCATTATCAACTCCTAATCCCAGCTCAGAAAGGGAGCCTCTGCGACTCATTCATCGCCCTCCAGGACTGACTGCATTGCACAGATGATGGATATTTACGTATGTTTGAAACGACCATCCTGGATGGTGGACAATAAAAGAATGAGGACTGCTTCAAATTTCCAGTGGCTGTTATCAACATTTATTCTTCTATATCTAATGAATCAAGTAAATAGCCAGAAAAAGGGGGCTCCTCATGATTTGAAGTGTGTAACTAACAATTTGCAAGTGTGGAACTGTTCTTGGAAAGCACCCTCTGGAACAGGCCGTGGTACTGATTATGAAGTTTGCATTGAAAACAGGTCCCGTTCTTGTTATCAGTTGGAGAAAACCAGTATTAAAATTCCAGCTCTTTCACATGGTGATTATGAAATAACAATAAATTCTCTACATGATTTTGGAAGTTCTACAAGTAAATTCACACTAAATGAACAAAACGTTTCCTTAATTCCAGATACTCCAGAGATCTTGAATTTGTCTGCTGATTTCTCAACCTCTACATTATACCTAAAGTGGAACGACAGGGGTTCAGTTTTTCCACACCGCTCAAATGTTATCTGGGAAATTAAAGTTCTACGTAAAGAGAGTATGGAGCTCGTAAAATTAGTGACCCACAACACAACTCTGAATGGCAAAGATACACTTCATCACTGGAGTTGGGCCTCAGATATGCCCTTGGAATGTGCCATTCATTTTGTGGAAATTAGATGCTACATTGACAATCTTCATTTTTCTGGTCTCGAAGAGTGGAGTGACTGGAGCCCTGTGAAGAACATTTCTTGGATACCTGATTCTCAGACTAAGGTTTTTCCTCAAGATAAAGTGATACTTGTAGGCTCAGACATAACATTTTGTTGTGTGAGTCAAGAAAAAGTGTTATCAGCACTGATTGGCCATACAAACTGCCCCTTGATCCATCTTGATGGGGAAAATGTTGCAATCAAGATTCGTAATATTTCTGTTTCTGCAAGTAGTGGAACAAATGTAGTTTTTACAACCGAAGATAACATATTTGGAACCGTTATTTTTGCTGGATATCCACCAGATACTCCTCAACAACTGAATTGTGAGACACATGATTTAAAAGAAATTATATGTAGTTGGAATCCAGGAAGGGTGACAGCGTTGGTGGGCCCACGTGCTACAAGCTACACTTTAGTTGAAAGTTTTTCAGGAAAATATGTTAGACTTAAAAGAGCTGAAGCACCTACAAACGAAAGCTATCAATTATTATTTCAAATGCTTCCAAATCAAGAAATATATAATTTTACTTTGAATGCTCACAATCCGCTGGGTCGATCACAATCAACAATTTTAGTTAATATAACTGAAAAAGTTTATCCCCATACTCCTACTTCATTCAAAGTGAAGGATATTAATTCAACAGCTGTTAAACTTTCTTGGCATTTACCAGGCAACTTTGCAAAGATTAATTTTTTATGTGAAATTGAAATTAAGAAATCTAATTCAGTACAAGAGCAGCGGAATGTCACAATCAAAGGAGTAGAAAATTCAAGTTATCTTGTTGCTCTGGACAAGTTAAATCCATACACTCTATATACTTTTCGGATTCGTTGTTCTACTGAAACTTTCTGGAAATGGAGCAAATGGAGCAATAAAAAACAACATTTAACAACAGAAGCCAGTCCTTCAAAGGGGCCTGATACTTGGAGAGAGTGGAGTTCTGATGGAAAAAATTTAATAATCTATTGGAAGCCTTTACCCATTAATGAAGCTAATGGAAAAATACTTTCCTACAATGTATCGTGTTCATCAGATGAGGAAACACAGTCCCTTTCTGAAATCCCTGATCCTCAGCACAAAGCAGAGATACGACTTGATAAGAATGACTACATCATCAGCGTAGTGGCTAAAAATTCTGTGGGCTCATCACCACCTTCCAAAATAGCGAGTATGGAAATTCCAAATGATGATCTCAAAATAGAACAAGTTGTTGGGATGGGAAAGGGGATTCTCCTCACCTGGCATTACGACCCCAACATGACTTGCGACTACGTCATTAAGTGGTGTAACTCGTCTCGGTCGGAACCATGCCTTATGGACTGGAGAAAAGTTCCCTCAAACAGCACTGAAACTGTAATAGAATCTGATGAGTTTCGACCAGGTATAAGATATAATTTTTTCCTGTATGGATGCAGAAATCAAGGATATCAATTATTACGCTCCATGATTGGATATATAGAAGAATTGGCTCCCATTGTTGCACCAAATTTTACTGTTGAGGATACTTCTGCAGATTCGATATTAGTAAAATGGGAAGACATTCCTGTGGAAGAACTTAGAGGCTTTTTAAGAGGATATTTGTTTTACTTTGGAAAAGGAGAAAGAGACACATCTAAGATGAGGGTTTTAGAATCAGGTCGTTCTGACATAAAAGTTAAGAATATTACTGACATATCCCAGAAGACACTGAGAATTGCTGATCTTCAAGGTAAAACAAGTTACCACCTGGTCTTGCGAGCCTATACAGATGGTGGAGTGGGCCCGGAGAAGAGTATGTATGTGGTGACAAAGGAAAATTCTGTGGGATTAATTATTGCCATTCTCATCCCAGTGGCAGTGGCTGTCATTGTTGGAGTGGTGACAAGTATCCTTTGCTATCGGAAACGAGAATGGATTAAAGAAACCTTCTACCCTGATATTCCAAATCCAGAAAACTGTAAAGCATTACAGTTTCAAAAGAGTGTCTGTGAGGGAAGCAGTGCTCTTAAAACATTGGAAATGAATCCTTGTACCCCAAATAATGTTGAGGTTCTGGAAACTCGATCAGCATTTCCTAAAATAGAAGATACAGAAATAATTTCCCCAGTAGCTGAGCGTCCTGAAGATCGCTCTGATGCAGAGCCTGAAAACCATGTGGTTGTGTCCTATTGTCCACCCATCATTGAGGAAGAAATACCAAACCCAGCCGCAGATGAAGCTGGAGGGACTGCACAGGTTATTTACATTGATGTTCAGTCGATGTATCAGCCTCAAGCAAAACCAGAAGAAGAACAAGAAAATGACCCTGTAGGAGGGGCAGGCTATAAGCCACAGATGCACCTCCCCATTAATTCTACTGTGGAAGATATAGCTGCAGAAGAGGACTTAGATAAAACTGCGGGTTACAGACCTCAGGCCAATGTAAATACATGGAATTTAGTGTCTCCAGACTCTCCTAGATCCATAGACAGCAACAGTGAGATTGTCTCATTTGGAAGTCCATGCTCCATTAATTCCCGACAATTTTTGATTCCTCCTAAAGATGAAGACTCTCCTAAATCTAATGGAGGAGGGTGGTCCTTTACAAACTTTTTTCAGAACAAACCAAACGATTAACAGTGTCACCGTGTCACTTCAGTCAGCCATCTCAATAAGCTCTTACTGCTAGTGTTGCTACATCAGCACTGGGCATTCTTGGAGGGATCCTGTGAAGTATTGTTAGGAGGTGAACTTCACTACATGTTAAGTTACACTGAAAGTTCATGTGCTTTTAATGTAGTCTAAAAGCCAAAGTATAGTGACTCAGAATCCTCAATCCACAAAACTCAAGATTGGGAGCTCTTTGTGATCAAGCCAAAGAATTCTCATGTACTCTACCTTCAAGAAGCATTTCAAGGCTAATACCTACTTGTACGTACATGTAAAACAAATCCCGCCGCAACTGTTTTCTGTTCTGTTGTTTGTGGTTTTCTCATATGTATACTTGGTGGAATTGTAAGTGGATTTGCAGGCCAGGGAGAAAATGTCCAAGTAACAGGTGAAGTTTATTTGCCTGACGTTTACTCCTTTCTAGATGAAAACCAAGCACAGATTTTAAAACTTCTAAGATTATTCTCCTCTATCCACAGCATTCACAAAAATTAATATAATTTTTAATGTAGTGACAGCGATTTAGTGTTTTGTTTGATAAAGTATGCTTATTTCTGTGCCTACTGTATAATGGTTATCAAACAGTTGTCTCAGGCGTAC

AATTTTAAGTGTCCGAATAAGATATGTCTTTTTTGTTTGTTTTTTTTGGTTGGTTGTTTGTTTTTTATCATCTGAGATTCTGTAATGTATTTGCAAATAATGGATCAATTAATTTTTTTTGAAGCTCATATTGTATCTTTTTAAAAACCATGTTGTGGAAAAAAGCCAGAGTGACAAGTGACAAAATCTATTTAGGAACTCTGTGTATGAATCCTGATTTTAACTGCTAGGATTCAGCTAAATTTCTGAGCTTTATGATCTGTGGAAATTTGGAATGAAATCGAATTCATTTTGTACATACATAGTATATTAAAACTATATAATAGTTCATAGAAATGTTCAGTAATGAAAAAATATATCCAATCAGAGCCATCCCGAAAAAAAAAAAAAAA

The FASTA file, including the sequence of NM_(—)002310, was masked usingthe RepeatMasker web interface (Smit, AFA & Green, P RepeatMasker athttp://ftp.genome.washington.edu/RM/RepeatMasker.html, Smit and Green).Specifically, during masking, the following types of sequences werereplaced with “N's”: SINE/MIR & LINE/L2, LINE/L1, LTR/MaLR,LTR/Retroviral, Alu, and other low informational content sequences suchas simple repeats. Below is the sequence following masking:

(SEQ ID NO: 8828).CTCTCTCCCAGAACGTGTCTCTGCTGCAAGGCACCGGGCCCTTTCGCTCTGCAGAACTGCACTTGCAAGACCATTATCAACTCCTAATCCCAGCTCAGAAAGGGAGCCTCTGCGACTCATTCATCGCCCTCCAGGACTGACTGCATTGCACAGATGATGGATATTTACGTATGTTTGAAACGACCATCCTGGATGGTGGACAATAAAAGAATGAGGACTGCTTCAAATTTCCAGTGGCTGTTATCAACATTTATTCTTCTATATCTAATGAATCAAGTAAATAGCCAGAAAAAGGGGGCTCCTCATGATTTGAAGTGTGTAACTAACAATTTGCAAGTGTGGAACTGTTCTTGGAAAGCACCCTCTGGAACAGGCCGTGGTACTGATTATGAAGTTTGCATTGAAAACAGGTCCCGTTCTTGTTATCAGTTGGAGAAAACCAGTATTAAAATTCCAGCTCTTTCACATGGTGATTATGAAATAACAATAAATTCTCTACATGATTTTGGAAGTTCTACAAGTAAATTCACACTAATTGAACAAAACGTTTCCTTAATTCCAGATACTCCAGAGATCTTGAATTTGTCTGCTGATTTCTCAACCTCTACATTATACCTAAAGTGGAACGACAGGGGTTCAGTTTTTCCACACCGCTCAAATGTTATCTGGGAAATTAAAGTTCTACGTAAAGAGAGTATGGAGCTCGTAAAATTAGTGACCCACAACACAACTCTGAATGGCAAAGATACACTTCATCACTGGAGTTGGGCCTCAGATATGCCCTTGGAATGTGCCATTCATTTTGTGGAAATTAGATGCTACATTGACAATCTTCATTTTTCTGGTCTCGAAGAGTGGAGTGACTGGAGCCCTGTGAAGAACATTTCTTGGATACCTGATTCTCAGACTAAGGTTTTTCCTCAAGATAAAGTGATACTTGTAGGCTCAGACATAACATTTTGTTGTGTGAGTCAAGAAAAAGTGTTATCAGCACTGATTGGCCATACAAACTGCCCCTTGATCCATCTTGATGGGGAAAATGTTGCAATCAAGATTCGTAATATTTCTGTTTCTGCAAGTAGTGGAACAAATGTAGTTTTTACAACCGAAGATAACATATTTGGAACCGTTATTTTTGCTGGATATCCACCAGATACTCCTCAACAACTGAATTGTGAGACACATGATTTAAAAGAAATTATATGTAGTTGGAATCCAGGAAGGGTGACAGCGTTGGTGGGCCCACGTGCTACAAGCTACACTTTAGTTGAAAGTTTTTCAGGAAAATATGTTAGACTTAAAAGAGCTGAAGCACCTACAAACGAAAGCTATCAATTATTATTTCAAATGCTTCCAAATCAAGAAATATATAATTTTACTTTGAATGCTCACAATCCGCTGGGTCGATCACAATCAACAATTTTAGTTAATATAACTGAAAAAGTTTATCCCCATACTCCTACTTCATTCAAAGTGAAGGATATTAATTCAACAGCTGTTAAACTTTCTTGGCATTTACCAGGCAACTTTGCAAAGATTAATTTTTTATGTGAAATTGAAATTAAGAAATCTAATTCAGTACAAGAGCAGCGGAATGTCACAATCAAAGGAGTAGAAAATTCAAGTTATCTTGTTGCTCTGGACAAGTTAAATCCATACACTCTATATACTTTTCGGATTCGTTGTTCTACTGAAACTTTCTGGAAATGGAGCAAATGGAGCAATAAAAAACAACATTTAACAACAGAAGCCAGTCCTTCAAAGGGGCCTGATACTTGGAGAGAGTGGAGTTCTGATGGAAAAAATTTAATAATCTATTGGAAGCCTTTACCCATTAATGAAGCTAATGGAAAAATACTTTCCTACAATGTATCGTGTTCATCAGATGAGGAAACACAGTCCCTTTCTGAAATCCCTGATCCTCAGCACAAAGCAGAGATACGACTTGATAAGAATGACTACATCATCAGCGTAGTGGCTAAAAATTCTGTGGGCTCATCACCACCTTCCAAAATAGCGAGTATGGAAATTCCAAATGATGATCTCAAAATAGAACAAGTTGTTGGGATGGGAAAGGGGATTCTCCTCACCTGGCATTACGACCCCAACATGACTTGCGACTACGTCATTAAGTGGTGTAACTCGTCTCGGTCGGAACCATGCCTTATGGACTGGAGAAAAGTTCCCTCAAACAGCACTGAAACTGTAATAGAATCTGATGAGTTTCGACCAGGTATAAGATATAATTTTTTCCTGTATGGATGCAGAAATCAAGGATATCAATTATTACGCTCCATGATTGGATATATAGAAGAATTGGCTCCCATTGTTGCACCAAATTTTACTGTTGAGGATACTTCTGCAGATTCGATATTAGTAAAATGGGAAGACATTCCTGTGGAAGAACTTAGAGGCTTTTTAAGAGGATATTTGTTTTACTTTGGAAAAGGAGAAAGAGACACATCTAAGATGAGGGTTTTAGAATCAGGTCGTTCTGACATAAAAGTTAAGAATATTACTGACATATCCCAGAAGACACTGAGAATTGCTGATCTTCAAGGTAAAACAAGTTACCACCTGGTCTTGCGAGCCTATACAGATGGTGGAGTGGGCCCGGAGAAGAGTATGTATGTGGTGACAAAGGAAAATTCTGTGGGATTAATTATTGCCATTCTCATCCCAGTGGCAGTGGCTGTCATTGTTGGAGTGGTGACAAGTATCCTTTGCTATCGGAAACGAGAATGGATTAAAGAAACCTTCTACCCTGATATTCCAAATCCAGAAAACTGTAAAGCATTACAGTTTCAAAAGAGTGTCTGTGAGGGAAGCAGTGCTCTTAAAACATTGGAAATGAATCCTTGTACCCCAAATAATGTTGAGGTTCTGGAAACTCGATCAGCATTTCCTAAAATAGAAGATACAGAAATAATTTCCCCAGTAGCTGAGCGTCCTGAAGATCGCTCTGATGCAGAGCCTGAAAACCATGTGGTTGTGTCCTATTGTCCACCCATCATTGAGGAAGAAATACCAAACCCAGCCGCAGATGAAGCTGGAGGGACTGCACAGGTTATTTACATTGATGTTCAGTCGATGTATCAGCCTCAAGCAAAACCAGAAGAAGAACAAGAAAATGACCCTGTAGGAGGGGCAGGCTATAAGCCACAGATGCACCTCCCCATTAATTCTACTGTGGAAGATATAGCTGCAGAAGAGGACTTAGATAAAACTGCGGGTTACAGACCTCAGGCCAATGTAAATACATGGAATTTAGTGTCTCCAGACTCTCCTAGATCCATAGACAGCAACAGTGAGATTGTCTCATTTGGAAGTCCATGCTCCATTAATTCCCGACAATTTTTGATTCCTCCTAAAGATGAAGACTCTCCTAAATCTAATGGAGGAGGGTGGTCCTTTACAAACTTTTTTCAGAACAAACCAAACGATTAACAGTGTCACCGTGTCACTTCAGTCAGCCATCTCAATAAGCTCTTACTGCTAGTGTTGCTACATCAGCACTGGGCATTCTTGGAGGGATCCTGTGAAGTATTGTTAGGAGGTGAACTTCACTACATGTTAAGTTACACTGAAAGTTCATGTGCTTTTAATGTAGTCTAAAAGCCAAAGTATAGTGACTCAGAATCCTCAATCCACAAAACTCAAGATTGGGAGCTCTTTGTGATCAAGCCAAAGAATTCTCATGTACTCTACCTTCAAGAAGCATTTCAAGGCTAATACCTACTTGTACGTACATGTAAAACAAATCCCGCCGCAACTGTTTTCTGTTCTGTTGTTTGTGGTTTTCTCATATGTATACTTGGTGGAATTGTAAGTGGATTTGCAGGCCAGGGAGAAAATGTCCAAGTAACAGGTGAAGTTTATTTGCCTGACGTTTACTCCTTTCTAGATGAAAACCAAGCACAGATTTTAAAACTTCTAAGATTATTCTCCTCTATCCACAGCATTCACNNNNNNNNNNNNNNNNNNNNNNGTAGTGACAGCGATTTAGTGTTTTGTTTGATAAAGTATGCTTATTTCTGTGCCTACTGTATAATGGTTATCAAACAGTTGTCTCAGGGGTAC

AAAAGTACTTGAAAATTTTAAGTGTCCGAATAAGATATGTCTTTTTTGTTTGTTTTTTTTGGTTGGTTGTTTGTTTTTTATCATCTGAGATTCTGTAATGTATTTGCAAATAATGGATCAATTAATTTTTTTTGAAGCTCATATTGTATCTTTTTAAAAACCATGTTGTGGAAAAAAGCCAGAGTGACAAGTGACAAAATCTATTTAGGAACTCTGTGTATGAATCCTGATTTTAACTGCTAGGATTCAGCTAAATTTCTGAGCTTTATGATCTGTGGAAATTTGGAATGAAATCGAATTCATTTTGTACATACATAGTATATTAAAACTATATAATAGTTCATAGAAATGTTCAGTAATGAAAAAATATATCCAATCAGAGCCATCCCGAAAAAAAAAAAAAAA

The length of this sequence was determined using batch, automatedcomputational methods and the sequence, as sense strand, its length, andthe desired location of the probe sequence near the 3′ end of the mRNAwas submitted to Array Designer Ver 1.1 (Premier Biosoft International,Palo Alto, Calif.). Search quality was set at 100%, number of bestprobes set at 1, length range set at 50 base pairs, Target Tm set at 75C. degrees plus or minus 5 degrees, Hairpin max deltaG at 6.0-kcal/mol.,Self dimmer max deltaG at 6.0-kcal/mol, Run/repeat (dinucleotide) maxlength set at 5, and Probe site minimum overlap set at 1. When none ofthe 49 possible probes met the criteria, the probe site would be moved50 base pairs closer to the 5′ end of the sequence and resubmitted toArray Designer for analysis. When no possible probes met the criteria,the variation on melting temperature was raised to plus and minus 8degrees and the number of identical basepairs in a run increased to 6 sothat a probe sequence was produced.

In the sequence above, using the criteria noted above, Array DesignerVer 1.1 designed a probe corresponding to oligonucleotide number 2280 inTable 8 and is indicated by underlining in the sequence above. It has amelting temperature of 68.4 degrees Celsius and a max run of 6nucleotides and represents one of the cases where the criteria for probedesign in Array Designer Ver 1.1 were relaxed in order to obtain anoligonucleotide near the 3′ end of the mRNA (Low melting temperature wasallowed).

Clone 463D12

Clone 463D12 was sequenced and compared to the nr, dbEST, and UniGenedatabases at NCBI using the BLAST search tool. The sequence matchedaccession number AI184553, an EST sequence with the definition line“qd60a05.x1 Soares_testis_NHT Homo sapiens cDNA clone IMAGE:1733840 3′similar to gb:M29550 PROTEIN PHOSPHATASE 2B CATALYTIC SUBUNIT 1 (HUMAN),mRNA sequence.” The E value of the alignment was 1.00×10⁻¹¹⁸. TheGenBank sequence begins with a poly-T region, suggesting that it is theantisense strand, read 5′ to 3′. The beginning of this sequence iscomplementary to the 3′ end of the mRNA sense strand. The accessionnumber for this sequence was included in a text file of accessionnumbers representing antisense sequences. Sequences for antisense strandmRNAs were obtained by uploading a text file containing desiredaccession numbers as an Entrez search query using the Batch Entrez webinterface and saving the results locally as a FASTA file. The followingsequence was obtained, and the region of alignment of clone 463D12 isoutlined:

(SEQ ID NO: 8829)TTTTTTTTTTTTTTCTTAAATAGCATTTATTTTCTCTCAAAAAGCCTATTATGTACTAACAAGTGTTCCTCTAAATTAGAAAGGCATCACTACTAAAATTTTATACATATTTTTTATATAAGAGAAGGAATATTGGGTTACAATCTGAATTTCTCTTTATGATTTCTCTTAAAGTATAGAACAGCTATTAAAATGACTAATATTGCTAAAATGAAGGCTACTAAATTTCCCCAAGAATTTCGGTGGAATGCCCAAAAATGGTGTTAAGATATGCAGAAGGGCCCATTTCAAGCAAAGCAA

AAAATTATACCTTTTTCTCCAACAAACGGTAAAGACCACGTGAAGACATCCATAAAATTAGGCAACCAGTAAAGATGTGGAGAACCAGTAAACTGTCGAAATTCATCACATTATTTTCATACTTTAATACAGCAGCTTTAATTATTGGAGAACATCAAAGTAATTAGGTGCCGAAAAACATTGTTATTAATGAAGGGAACCCCTGACGTTTGACCTTTTCTGTACCATCTATAGCCCTGG ACTTGA

The FASTA file, including the sequence of AA184553, was then maskedusing the RepeatMasker web interface, as shown below. The region ofalignment of clone 463D12 is outlined.

Masked version of 463D12 sequence. (SEQ ID NO: 8830)TTTTTTTTTTTTTTCTTAAATAGCATTTATTTTCTCTCAAAAAGCCTATTATGTACTAACAAGTGTTCCTCTAAATTAGAAAGGCATCACTACNNNNNNNNNNNNNNNNNNNNNNNNNNNNGAGAAGGAATATTGGGTTACAATCTGAATTTCTCTTTATGATTTCTCTTAAAGTATAGAACAGCTATTAAAATGACTAATATTGCTAAAATGAAGGCTACTAAATTTCCCCAAGAATTTCGGTGGAATGCCCAAAAATGGTGTTAAGATATGCAGAAGGGCCCATTTCAAGCAAAGCAA

NNNNNNNNNCCTTTTTCTCCAACAAACGGTAAAGACCACGTGAAGACATCCATAAAATTAGGCAACCAGTAAAGATGTGGAGAACCAGTAAACTGTCGAAATTCATCACATTATTTTCATACTTTAATACAGCAGCTTTAATTATTGGAGAACATCAAAGTAATTAGGTGCCGAAAAACATTGTTATTAATGAAGGGAACCCCTGACGTTTGACCTTTTCTGTACCATCTATAGCCCTGG ACTTGA 

The sequence was submitted to Array Designer as described above,however, the desired location of the probe was indicated at base pair 50and if no probe met the criteria, moved in the 3′ direction. Thecomplementary sequence from Array Designer was used, because theoriginal sequence was antisense. The oligonucleotide designed by ArrayDesigner corresponds to oligonucleotide number 4342 in Table 8 and iscomplementary to the underlined sequence above. The probe has a meltingtemperature of 72.7 degrees centigrade and a max run of 4 nucleotides.

Clone 72D4

Clone 72D4 was sequenced and compared to the nr, dbEST, and UniGenedatabases at NCBI using the BLAST search tool. No significant matcheswere found in any of these databases. When compared to the human genomedraft, significant alignments were found to three consecutive regions ofthe reference sequence NT_(—)008060, as depicted below, suggesting thatthe insert contains three spliced exons of an unidentified gene.

Residue numbers on Matching residue

clone 72D4 sequence numbers on NT_008060  1-198 478646-478843 197-489479876-480168 491-85  489271-489365

Because the reference sequence contains introns and may represent eitherthe coding or noncoding strand for this gene, BioCardia's own sequencefile was used to design the oligonucleotide. Two complementary probeswere designed to ensure that the sense strand was represented. Thesequence of the insert in clone 72D4 is shown below, with the threeputative exons outlined.

(SEQ ID NO: 8545)

The sequence was submitted to RepeatMasker, but no repetitive sequenceswere found. The sequence shown above was used to design the two 50-merprobes using Array Designer as described above. The probes are shown inbold typeface in the sequence depicted below. The probe in the sequenceis oligonucleotide number 6415 (SEQ ID NO: 6415) in Table 8 and thecomplementary probe is oligonucleotide number 6805 (SEQ ID NO:6805).

(SEQ ID NO: 6805) CAGGTCACACAGCACATCAGTGGCTACATGTGAGCTCAGACCTGGGTCTGCTGCTGTCTGTCTTCCCAATATCCATGACCTTGACTGATGCAGGTGTCTAGGGATACGTCCATCCCCGTCCTGCTGGAGCCCAGAGCACGGAAGCCTGGCCCTCCGAGGAGACAGAAGGGAGTGTCGGACACCATGACGAGAGCTTGGCAGAATAAATAACTTCTTTAAACAATTTTACGGCATGAAGAAATCTGGACCAGTTTATTAAATGGGATTTCTGCCACAAACCTTGGAAGAATCACATCATCTTANNCCCAAGTGAAAACTGTGTTGCGTAACAAAGAACATGACTGCGCTCCACACATACATCATTGCCCGGCGAGGCGGGACACAAGTCAACGACGGAACACTTGAGACAGGCCTACAACTGTGCACGGGTCAGAAGCAAGTTTAAGCCATACTTGCTGCAGTGAGACTACATTTCTGTCTATAGAAGATACCTGACTTGATCTGTTTTTCAGCTCCAGTTCCCAGATG TGC←----3′-GTCAAGGGTCTACACG GTGTTGTGGTCCCCAAGTATCACCTTCCAATTTCTGGGAG--→CACAACACCAGGGGTTCATAGTGGAAGGTTAAAG-5′ (SEQ ID NO: 8545)CAGTGCTCTGGCCGGATCCTTGCCGCGCGGATAAAAACT---→

Confirmation of Probe Sequence

Following probe design, each probe sequence was confirmed by comparingthe sequence against dbEST, the UniGene cluster set, and the assembledhuman genome using BLASTn at NCBI. Alignments, accession numbers, ginumbers, UniGene cluster numbers and names were examined and the mostcommon sequence used for the probe. The final probe set was compiledinto Table 8.

Example 22 Production of an Array of 8000 Spotted 50mer Oligonucleotides

We produced an array of 8000 spotted 50mer oligonucleotides. Examples 20and 21 exemplify the design and selection of probes for this array.

Sigma-Genosys (The Woodlands, Tex.) synthesized un-modified 50-meroligonucleotides using standard phosphoramidite chemistry, with astarting scale of synthesis of 0.05 μmole (see, e.g., R. Meyers, ed.(1995) Molecular Biology and Biotechnology: A Comprehensive DeskReference). Briefly, to begin synthesis, a 3′ hydroxyl nucleoside with adimethoxytrityl (DMT) group at the 5′ end was attached to a solidsupport. The DMT group was removed with trichloroacetic acid (TCA) inorder to free the 5′-hydroxyl for the coupling reaction. Next, tetrazoleand a phosphoramidite derivative of the next nucleotide were added. Thetetrazole protonates the nitrogen of the phosphoramidite, making itsusceptible to nucleophilic attack. The DMT group at the 5′-end of thehydroxyl group blocks further addition of nucleotides in excess. Next,the inter-nucleotide linkage was converted to a phosphotriester bond inan oxidation step using an oxidizing agent and water as the oxygendonor. Excess nucleotides were filtered out and the cycle for the nextnucleotide was started by the removal of the DMT protecting group.Following the synthesis, the oligo was cleaved from the solid support.The oligonucleotides were desalted, resuspended in water at aconcentration of 100 or 200 μM, and placed in 96-deep well format. Theoligonucleotides were re-arrayed into Whatman Uniplate 384-wellpolyproylene V bottom plates. The oligonucleotides were diluted to afinal concentration 30 μM in 1× Micro Spotting Solution Plus(Telechem/arrayit.com, Sunnyvale, Calif.) in a total volume of 15 μl. Intotal, 8,031 oligonucleotides were arrayed into twenty-one 384-wellplates.

Arrays were produced on Telechem/arrayit.com Super amine glasssubstrates (Telechem/arrayit.com), which were manufactured in 0.1 mmfiltered clean room with exact dimensions of 25×76×0.96 mm. The arrayswere printed using the Virtek Chipwriter with a Telechem 48 pin MicroSpotting Printhead. The Printhead was loaded with 48 Stealth SMP3BTeleChem Micro Spotting Pins, which were used to print oligonucleotidesonto the slide with the spot size being 110-115 microns in diameter.

Example 23 Amplification, Labeling, and Hybridization of Total RNA to anOligonucleotide Microarray

Amplification, Labeling, Hybridization and Scanning

Samples consisting of at least 2 μg of intact total RNA were furtherprocessed for array hybridization. Amplification and labeling of totalRNA samples was performed in three successive enzymatic reactions.First, a single-stranded DNA copy of the RNA was made (hereinafter,“ss-cDNA”). Second, the ss-cDNA was used as a template for thecomplementary DNA strand, producing double-stranded cDNA (hereinafter,“ds-cDNA, or cDNA”). Third, linear amplification was performed by invitro transcription from a bacterial T₇ promoter. During this step,fluorescent-conjugated nucleotides were incorporated into the amplifiedRNA (hereinafter, “aRNA”).

The first strand cDNA was produced using the Invitrogen kit (SuperscriptII). The first strand cDNA was produced in a reaction composed of 50 mMTris-HCl (pH 8.3), 75 mM KCl, and 3 mM MgCl₂ (1× First Strand Buffer,Invitrogen), 0.5 mM dGTP, 0.5 mM dATP, 0.5 mM dTTP, 0.5 mM dCTP, 10 mMDTT, 10 U reverse transcriptase (Superscript II, Invitrogen, #18064014),15 U RNase inhibitor (RNAGuard, Amersham Pharmacia, #27-0815-01), 5 μMT7T24 primer(5′-GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGGTTTTTTTTTTTTTTTTTTTTTTTT-3′),(SEQ ID NO:8831) and 2 μg of selected sample total RNA. Severalpurified, recombinant control mRNAs from the plant Arabidopsis thalianawere added to the reaction mixture: 2-20 pg of the following genes CAB,RCA, LTP4, NAC1, RCP1, XCP2, RBCL, LTP6, TIM, and PRKase (Stratagene,#252201, #252202, #252204, #252208, #252207, #252206, #252203, #252205,#252209, #252210 respectively). The control RNAs allow the estimate ofcopy numbers for individual mRNAs in the clinical sample becausecorresponding sense oligonucleotide probes for each of these plant genesare present on the microarray. The final reaction volume of 20 μl wasincubated at 42° C. for 60 min.

For synthesis of the second cDNA strand, DNA polymerase and RNase wereadded to the previous reaction, bringing the final volume to 150 μl. Theprevious contents were diluted and new substrates were added to a finalconcentration of 20 mM Tris-HCl (pH 7.0) (Fisher Scientific, Pittsburgh,Pa. #BP1756-100), 90 mMKCl (Teknova, Half Moon Bay, Calif., #0313-500),4.6 mM MgCl₂ (Teknova, Half Moon Bay, Calif., #0304-500), 10 mM(NH₄)₂SO₄(Fisher Scientific #A702-500)(1× Second Strand buffer, Invitrogen),0.266 mM dGTP, 0.266 mM dATP, 0.266 mM dTTP, 0.266 mM dCTP, 40 U E. coliDNA polymerase (Invitrogen, #18010-025), and 2 U RNaseH (Invitrogen,#18021-014). The second strand synthesis took place at 16° C. for 120minutes.

Following second-strand synthesis, the ds-cDNA was purified from theenzymes, dNTPs, and buffers before proceeding to amplification, usingphenol-chloroform extraction followed by ethanol precipitation of thecDNA in the presence of glycogen.

Alternatively, a silica-gel column is used to purify the cDNA (e.g.Qiaquick PCR cleanup from Qiagen, #28104). The cDNA was collected bycentrifugation at >10,000×g for 30 minutes, the supernatant isaspirated, and 150 μl of 70% ethanol, 30% water was added to wash theDNA pellet. Following centrifugation, the supernatant was removed, andresidual ethanol was evaporated at room temperature.

Linear amplification of the cDNA was performed by in vitro transcriptionof the cDNA. The cDNA pellet from the step described above wasresuspended in 7.4 μl of water, and in vitro transcription reactionbuffer was added to a final volume of 20 μl containing 7.5 mM GTP, 7.5mM ATP, 7.5 mM TTP, 2.25 mM CTP, 1.025 mM Cy3-conjugated CTP (PerkinElmer; Boston, Mass., #NEL-580), 1× reaction buffer (Ambion, MegascriptKit, Austin, Tex. and #1334) and 1% T₇ polymerase enzyme mix (Ambion,Megascript Kit, Austin, Tex. and #1334). This reaction was incubated at37° C. overnight. Following in vitro transcription, the RNA was purifiedfrom the enzyme, buffers, and excess NTPs using the RNeasy kit fromQiagen (Valencia, Calif.; # 74106) as described in the vendor'sprotocol. A second elution step was performed and the two eluates werecombined for a final volume of 60 μl. RNA is quantified using an Agilent2100 bioanalyzer with the RNA 6000 nano LabChip.

Reference RNA was prepared as described above, except Cy5-CTP wasincorporated instead of Cy3CTP. Reference RNA from five reactions, eachreaction started with 2 ug total RNA, was pooled together andquantitated as described above.

Hybridization to an Array

RNA was prepared for hybridization as follows: for an 18 mm×55 mm array,20 μg of amplified RNA (aRNA) was combined with 20 μg of reference aRNA.The combined sample and reference aRNA was concentrated by evaporatingthe water to 10 μl in a vacuum evaporator. The sample was fragmented byheating the sample at 95° C. for 30 minutes to fragment the RNA into50-200 bp pieces. Alternatively, the combined sample and reference aRNAwas concentrated by evaporating the water to 5 μl in a vacuumevaporator. Five μl of 20 mM zinc acetate was added to the aRNA and themix incubated at 60° C. for 10 minutes. Following fragmentation, 40 μlof hybridization buffer was added to achieve final concentrations of5×SSC and 0.20% SDS with 0.1 μg/ul of Cot-1 DNA (Invitrogen) as acompetitor DNA. The final hybridization mix was heated to 98° C., andthen reduced to 50° C. at 0.1° C. per second.

Alternatively, formamide is included in the hybridization mixture tolower the hybridization temperature.

The hybridization mixture was applied to a pre-heated 65° C. microarray,surface, covered with a glass coverslip (Corning, #2935-246), and placedon a pre-heated 65° C. hybridization chamber (Telechem, AHC-10). 15 ulof 5×SSC was placed in each of the reservoir in the hybridizationchamber and the chamber was sealed and placed in a water bath at 62° C.for overnight (16-20 hrs). Following incubation, the slides were washedin 2×SSC, 0.1% SDS for five minutes at 30° C., then in 2×SSC for fiveminutes at 30° C., then in 2×SSC for another five minutes at 30° C.,then in 0.2×SSC for two minutes at room temperature. The arrays werespun at 1000×g for 2 minutes to dry them. The dry microarrays are thenscanned by methods described above.

The microarrays were imaged on the Agilent (Palo Alto, Calif.) scannerG2565AA. The scan settings using the Agilent software were as follows:for the PMT Sensitivity (100% Red and 100% Green); Scan Resolution (10microns); red and green dye channels; used the default scan region forall slides in the carousel; using the largest scan region; scan date forInstrument ID; and barcode for Slide ID. The full image produced by theAgilent scanner was flipped, rotated, and split into two images (one foreach signal channel) using TIFFSplitter (Agilent, Palo Alto, Calif.).The two channels are the output at 532 nm (Cy3-labeled sample) and 633nm (Cy5-labeled R50). The individual images were loaded into GenePix 3.0(Axon Instruments, Union City, Calif.) for feature extraction, eachimage was assigned an excitation wavelength corresponding the fileopened; Red equals 633 nm and Green equals 532 nm. The setting file(gal) was opened and the grid was laid onto the image so that each spotin the grid overlaped with >50% of the feature. Then the GenePixsoftware was used to find the features without setting minimum thresholdvalue for a feature. For features with low signal intensity, GenePixreports “not found”. For all features, the diameter setting was adjustedto include only the feature if necessary.

The GenePix software determined the median pixel intensity for eachfeature (Fi) and the median pixel intensity of the local background foreach feature (B_(i)) in both channels. The standard deviation (SDF_(i)and SD13_(i)) for each is also determined. Features for which GenePixcould not discriminate the feature from the background were “flagged” asdescribed below.

Following feature extraction into a .gpr file, the header information ofthe .gpr file was changed to carry accurate information into thedatabase. An Excel macro was written to change the headers. The steps inthat macro were:

-   -   1. Open .gpr file.    -   2. Check the value in the first row, first column. If it is        “ATF”, then the header has likely already been reformatted. The        file is skipped and the user is alerted. Otherwise, proceed        through the following steps.    -   3. Store the following values in variables.        -   a. Name of .tif image file: parsed from row 11.        -   b. SlideID: parsed from name of .tif image file.        -   c. Version of the feature extraction software: parsed from            row 25        -   d. GenePix Array List file: parsed from row 6        -   e. GenePix Settings file: parsed from row 5    -   4. Delete rows 1-8, 10-12, 20, 22, and 25.    -   5. Arrange remaining values in rows 15-29.    -   6. Fill in rows 1-14 with the following:        -   Row 1: ScanID (date image file was last modified, formatted            as yyyy.mm.dd-hh.mm.ss)        -   Row 2: SlideID, from stored value        -   Row 3: Name of person who scanned the slide, from user input        -   Row 4: Image file name, from stored value        -   Row 5: Green PMT setting, from user input        -   Row 6: Red PMT setting, from user input        -   Row 7: ExtractID (date .gpr file was created, formatted as            yyyy.mm.dd-hh.mm.ss)        -   Row 8: Name of person who performed the feature extraction,            from user input        -   Row 9: Feature extraction software used, from stored value        -   Row 10: Results file name (same as the .gpr file name)        -   Row 11: GenePix Array List file, from stored value        -   Row 12: GenePix Settings file, from stored value        -   Row 13: StorageCD, currently left blank        -   Row 14: Extraction comments, from user input (anything about            the scanning or feature extraction of the image the user            feels might be relevant when selecting which hybridizations            to include in an analysis)            Pre-Processing with Excel Templates

Following analysis of the image and extraction of the data, the datafrom each hybridization was pre-processed to extract data that wasentered into the database and subsequently used for analysis. Thecomplete GPR file produced by the feature extraction in GenePix wasimported into an excel file pre-processing template. The same exceltemplate was used to process each GPR file. The template performs aseries of calculations on the data to differentiate poor features fromothers and to combine triplicate feature data into a single data pointfor each probe.

Each GPR file contained 31 rows of header information, followed by rowsof data for 24093 features. The last of these rows was retained with thedata. Rows 31 through the end of the file were imported into the exceltemplate. Each row contained 43 columns of data. The only columns usedin the pre-processing were: Oligo ID, F633 Median (median value from allthe pixels in the feature for the Cy5 dye), B633 Median (the medianvalue of all the pixels in the local background of the selected featurefor Cy5), B633 SD (the standard deviation of the values for the pixelsin the local background of the selected feature for Cy5), F532 Median(median value from all the pixels in the feature for the Cy3 dye), B532Median (the median value of all the pixels in the local background ofthe selected feature for Cy3), B532 SD (the standard deviation of thevalues for the pixels in the local background of the selected featurefor Cy3), and Flags. The GenePix Flags column contains the flags setduring feature extraction. “−75” indicates there were no featuresprinted on the array in that position, “−50” indicates that GenePixcould not differentiate the feature signal from the local background,and “−100” indicates that the user marked the feature as bad.

Once imported, the rows with −75 flags were deleted. Then the median ofB633 SD and B532 SD were calculated over all features with a flag valueof “0”. The minimum values of B633 Median and B532 Median wereidentified, considering only those values associated with a flag valueof “0”. For each feature, the signal to noise ratio (S/N) was calculatedfor both dyes by taking the fluorescence signal minus the localbackground (BGSS) and dividing it by the standard deviation of the localbackground:

${S/N} = \frac{F_{i} - B_{i}}{{SDB}_{i}}$

If the S/N was less than 3, then an adjusted background-subtractedsignal was calculated as the fluorescence minus the minimum localbackground on the slide. An adjusted S/N was then calculated as theadjusted background subtracted signal divided by the median noise overall features for that channel. If the adjusted S/N was greater thanthree and the original S/N were less than three, a flag of 25 was setfor the Cy5 channel, a flag of 23 was set for the Cy3 channel, and ifboth met these criteria, then a flag of 20 was set. If both the adjustedS/N and the original S/N were less than three, then a flag of 65 was setfor Cy5, 63 set for Cy3, and 60 set if both dye channels had an adjustedS/N less than three. All signal to noise calculations, adjustedbackground-subtracted signal, and adjusted S/N were calculated for eachdye channel. If the BGSS value was greater than or equal to 64000, aflag was set to indicate saturation; 55 for Cy5, 53 for Cy3, 50 forboth.

The BGSS used for further calculations was the original BGSS if theoriginal S/N was greater than or equal to three. If the original S/Nratio was less than three and the adjusted S/N ratio was greater than orequal to three, then the adjusted BGSS was used. If the adjusted S/Nratio was less than three, then the adjusted BGSS was used, but withknowledge of the flag status.

To facilitate comparison among arrays, the Cy3 and Cy5 data were scaledto have a median of 1. For each dye channel, the median value of allfeatures with flags=0, 20, 23, or 25 was calculated. The BGSS for eachdye in each feature was then divided by this median value. The Cy3/Cy5ratio was calculated for each feature using the scaled BGSS:

$R_{n} = \frac{{Cy}\; 3\; S_{i}}{{Cy}\; 5S_{i}}$

The flag setting for each feature was used to determine the expressionratio for each probe, a combination of three features. If all threefeatures had flag settings in the same category (categories=negatives, 0to 25, 50-55, and 60-65), then the average and CV of the three featureratios was calculated. If the CV of all three features was less than 15,the average was used. If the CV was greater than 15, then the CV of eachcombination of two of the features was calculated and the two featureswith the lowest CV were averaged. If none of the combinations of twofeatures had a CV less than 15, then the median ratio of the threefeatures was used as the probe feature.

If the three features do not have flags in the same category, then thefeatures with the best quality flags were used(0>25>23>20>55>53>50>65>63>60). Features with negative flags were neverused. When the best flags were two features in the same category, theaverage was used. If a single feature had a better flag category thanthe other two then that feature was used.

Once the probe expression ratio was calculated from the three features,the log of the ratio was taken as described below and stored for use inanalyzing the data. Whichever features were used to calculate the probevalue, the worst of the flags from those features was carried forwardand stored as the flag value for that probe. 2 different data sets canbe used for analysis. Flagged data uses all values, including those withflags. Filtered data sets are created by removing flagged data from theset before analysis.

Example 24 Identification of Diagnostic Nucleotide Sets for Diagnosis ofCardiac Allograft Rejection

Genes were identified which have expression patterns useful for thediagnosis and monitoring of cardiac allograft rejection. Further, setsof genes that work together in a diagnostic algorithm for allograftrejection were identified. Patients, patient clinical data and patientsamples used in the discovery of markers below were derived from aclinical study described in example 11.

The collected clinical data is used to define patient or sample groupsfor correlation of expression data. Patient groups are identified forcomparison, for example, a patient group that possesses a useful orinteresting clinical distinction, verses a patient group that does notpossess the distinction. Measures of cardiac allograft rejection arederived from the clinical data described above to divide patients (andpatient samples) into groups with higher and lower rejection activityover some period of time or at any one point in time. Such data arerejection grade as determined from pathologist reading of the cardiacbiopsies and data measuring progression of end-organ damage, includingdepressed left ventricular dysfunction (decreased cardiac output,decreased ejection fraction, clinical signs of low cardiac output) andusage of inotropic agents (Kobashigawa 1998).

Expression profiles correlating with occurrence of allograft rejectionare identified, including expression profiles corresponding to end-organdamage and progression of end-organ damage. Expression profiles areidentified predicting allograft rejection, and response to treatment orlikelihood of response to treatment. Subsets of the candidate library(or a previously identified diagnostic nucleotide set) are identified,that have predictive value for the presence of allograft rejection orprediction of allograft rejection or end organ damage.

Identification of a Diagnostic Nucleotide Set for Diagnosis of CardiacAllograft Acute Rejection

Mononuclear RNA samples were collected from patients who had recentlyundergone a cardiac allograft transplantation using the protocoldescribed in example 8. The allograft rejection status at the time ofsample collection was determined by examination of cardiac biopsies asdescribed in example 11.

180 samples were included in the analysis. Each patient sample wasassociated with a biopsy and clinical data collected at the time of thesample. The cardiac biopsies were graded by a pathologist at the localcenter and by a centralized pathologist who read the biopsy slides fromall four local centers in a blinded manner. Biopsy grades included 0,1A, 1B, 2, 3A, and 3B. No grade 4 rejection was identified. Dependentvariables were developed based on these grades using either the localcenter pathology reading or the higher of the two readings, local orcentralized. The dependent variables used for correlation of geneexpression profiles with cardiac allograft rejection are shown in Table13. Dependent variables are used to create classes of samplescorresponding to the presence or absence of rejection.

Clinical data were also used to determine criteria for including samplesin the analysis. The strictest inclusion criteria required that samplesbe from patients who did not have a bacterial or viral infection, wereat least two weeks post cardiac transplant and were not currentlyadmitted to the hospital. A second inclusion criteria (inclusion 2)reduced the post-transplant criteria to 1 week and eliminated thehospital admission criteria.

After preparation of RNA (example 8), amplification, labeling,hybridization, scanning, feature extraction and data processing weredone as described in Example 23, using the oligonucleotide microarraysdescribed in Examples 20-22. The resulting log ratio of expression ofCy3 (patient sample)/Cy5 (R50 reference RNA) was used for analysis. Thisdataset is called the “static” data. A second type of dataset,referenced, was derived from the first. These datasets compared the geneexpression log ratio in each sample to a baseline sample from the samepatient using the formula:

ref log ratio=(log ratio_(sample))−(log ratio_(baseline))

Two referenced datasets were used, named “0 HG” and “Best 0”. Thebaseline for 0 HG was a Grade 0 sample from the same patient as thesample, using the highest grade between the centralized and localpathologists. The baseline for Best 0 was a Grade 0 sample from the samepatient as the sample, using both the local and centralized readerbiopsy grade data. When possible a Grade 0 prior to the sample was usedas the baseline in both referenced datasets.

The datasets were also divided into subsets to compare analysis betweentwo subsets of roughly half of the data. The types of subsetsconstructed were as follows. First half/second half subsets were thefirst half of the samples and the second half of the samples from adataset ordered by sample number. Odd/even subsets used the same source,a dataset ordered by sample number, but the odd subset consisted ofevery 2^(nd) sample starting with the first and the even subsetconsisted of every 2^(nd) sample starting with the second sample, Center14/other subsets were the same datasets, divided by transplant hospital.The center 14 subset consisted of all samples from patients at center14, while the other subset consisted of all samples from the other threecenters (12,13, and 15).

Initially, significance analysis for microarrays (SAM, Tusher 2001,Example 26) was used to discover genes that were differentiallyexpressed between the rejection and no-rejection groups. Ninety-sixdifferent combinations of dependent variables, inclusion criteria,static/referenced, and data subsets were used in SAM analysis to developthe primary lists of genes significantly differentially expressedbetween rejection and no-rejection. The most significant of these geneswere chosen based on the following criteria. Tier 1 (A1, in Table 12A)genes were those which appeared with an FDR of less than 20% inidentical analyses in two independent subsets. Tier 2 (A2 in Table 12A)genes were those which appeared in the top 20 genes on the list with anFDR less than 20% more than 50% of the time over all dependent variableswith the inclusion criteria, and static/referenced constant. Tier 3genes were those that appeared more than 50% of the time with an FDRless than 20% more than 50% of the time over all dependent variableswith the inclusion criteria, and static/referenced constant. The genesthat were identified by the analysis as statistically differentiallyexpressed between rejection and no rejection are shown in Table 12A.

SAM chooses genes as significantly different based on the magnitude ofthe difference between the groups and the variation among the sampleswithin each group. An example of the difference between some Grade 0 andsome Grade 3A samples for 9 genes is show in FIG. 9A.

Additionally, many of these same combinations were used in theSupervised Harvesting of Expression Trees (SHET, Hastie et al. 2001)algorithm (see example 26) to identify markers that the algorithm choseas the best to distinguish between the rejection and no rejectionclasses using a bias factor of 0.01. The top 20 or 30 terms were takenfrom the SHET output and among all comparisons in either the static orreferenced data the results were grouped. Any gene found in the top 5terms in more than 50% of the analyses was selected to be in group B1(Table 12A). The occurrences of each gene were tabulated over all SHETanalysis (for either static or referenced data) and the 10 genes thatoccurred the most were selected to be in group B2 (Table 12A).

An additional classification method used was CART (Salford Systems, SanDiego, example 26). Either the static or referenced dataset was reducedto only the genes for which expression values (log ratios) were presentin at least 80% of the samples. These data were used in CART with thedefault settings, using the Symmetric Gini algorithm. Each of thedependent variables was used with both the full sample set and thestrict inclusion criteria. Two groups of genes were identified. Group C1were those genes that were a primary splitter (1^(st) decision node).Group C2 genes were the 10 genes that occurred as splitters the mostoften over all these analyses.

Two other classification models were developed and their best genesidentified as markers of cardiac allograft rejection. Group D genes wereidentified from a set of 59 samples, referenced data, local biopsyreading grade, using logistic regression. Group E genes were identifiedfrom the primary static dataset using a K-nearest neighborclassification algorithm.

Both hierarchical clustering (Eisen et al. 1998) and CART were used toidentify surrogates for each identified marker as shown in Table 12C.Hierarchical clustering surrogates are genes co-expressed in these andwere chosen from the nearest branches of the dendrogram. CART surrogateswere identified by CART as the surrogates for those genes chosen asprimary splitters at decision nodes.

Primers for real-time PCR validation were designed for each of themarker genes as described in Example 25 and are listed in Table 12B.

CART was used to build a decision tree for classification of samples asrejection or no-rejection using the gene expression data from thearrays. The analysis identified sets of genes that can be used togetherto accurately identify samples derived from cardiac allograft transplantpatients. The set of genes and the identified threshold expressionlevels for the decision tree are referred to as a “models”. This modelcan be used to predict the rejection state of an unknown sample. Theinput data were the static expression data (log ratio) and thereferenced expression data (log ratio referenced to the best availablegrade 0 from either the centralized reader or the local reader) for 139of our top marker genes.

These two types of expression data were entered into the CART softwareas independent variables. The dependent variable was rejection state,defined for this model as no rejection=grade 0 and rejection=grade 3A.Samples were eliminated from consideration in the training set if theywere from patients with either bacterial or viral infection or were frompatients who were less than two weeks post-transplant. The method usedwas Symmetric Gini, allowing linear combinations of independentvariables. The costs were set to 1 for both false negatives and falsepositives and the priors were set equal for the two states. No penaltieswere assessed for missing data, however the marker genes selected havestrong representation across the dataset. 10-fold cross validation wasused to test the model. Settings not specified remained at the defaultvalues.

The model shown in FIG. 9B is based on decisions about expression valuesat three nodes, each a different marker gene. The cost assigned to thismodel is 0.292, based on the priors being equal, the costs set to 1 foreach type of error, and the results from the 10-fold cross validation.

In the training set, no rejection samples were misclassified(sensitivity=100%) and only 1 no-rejection sample was misclassified(specificity=94.4%). Following 10-fold cross validation, 2 rejectionsamples were misclassified (sensitivity=87.5%) and 3 no-rejectionsamples were misclassified (specificity=83.3%). The CART softwareassigns surrogate markers for each decision node. For this model, thesurrogates are shown in FIG. 9C and Table 12C.

These genes can be used alone or in association with other genes orvariables to build a diagnostic gene set or a classification algorithm.These genes can be used in association with known gene markers forrejection (such as those identified in the prior art) to provide adiagnostic algorithm.

Example 25 Real-Time PCR Validation of Array Expression Results

In examples 17 and 24, leukocyte gene expression was used to discoverexpression markers and diagnostic gene sets for clinical outcomes. It isdesirable to validate the gene expression results for each gene using amore sensitive and quantitative technology such as real-time PCR.Further, it is possible for the diagnostic nucleotide sets to beimplemented as a diagnostic test as a real-time PCR panel.Alternatively, the quantitative information provided by real-time PCRvalidation can be used to design a diagnostic test using any alternativequantitative or semi-quantitative gene expression technology.

To validate the results of the microarray experiments we used real-time,or kinetic, PCR. In this type of experiment the amplification product ismeasured during the PCR reaction. This enables the researcher to observethe amplification before any reagent becomes rate limiting foramplification. In kinetic PCR the measurement is of C_(T) (thresholdcycle) or C_(P) (crossing point). This measurement (C_(T)=C_(P)) is thepoint at which an amplification curve crosses a threshold fluorescencevalue. The threshold is set to a point within the area where all of thereactions were in their linear phase of amplification. When measuringC_(T), a lower C_(T) value is indicative of a higher amount of startingmaterial since an earlier cycle number means the threshold was crossedmore quickly.

Several fluorescence methodologies are available to measureamplification product in real-time PCR. Taqman (Applied BioSystems,Foster City, Calif.) uses fluorescence resonance energy transfer (FRET)to inhibit signal from a probe until the probe is degraded by thesequence specific binding and Taq 3′ exonuclease activity. MolecularBeacons (Stratagene, La Jolla, Calif.) also use FRET technology, wherebythe fluorescence is measured when a hairpin structure is relaxed by thespecific probe binding to the amplified DNA. The third commonly usedchemistry is Sybr Green, a DNA-binding dye (Molecular Probes, Eugene,Oreg.). The more amplified product that is produced, the higher thesignal. The Sybr Green method is sensitive to non-specific amplificationproducts, increasing the importance of primer design and selection.Other detection chemistries can also been used, such as ethedium bromideor other DNA-binding dyes and many modifications of the fluorescentdye/quencher dye Taqman chemistry.

Initially, samples are chosen for validation, which have already beenused for microarray based expression analysis. They are also chosen torepresent important disease classes or disease criteria. For the firststeps of this example (primer design, primer endpoint testing, andprimer efficiency testing) we examined β-actin and β-GUS. These genesare considered “housekeeping” genes because they are required formaintenance in all cells. They are commonly used as a reference that isexpected to not change with experimental treatment. We chose these twoparticular genes as references because they varied the least inexpression across 5 mRNA samples examined by real-time PCR.

The inputs for real time PCR reaction are gene-specific primers, cDNAfrom specific patient samples, and the standard reagents. The cDNA wasproduced from mononuclear RNA (prepared as in example 8) by reversetranscription using OligodT primers (Invitrogen, 18418-012) and randomhexamers (Invitrogen, 48190-011) at a final concentration of 0.5 ng/μland 3 ng/μl respectively. For the first strand reaction mix, 1.45 μg/μlof total RNA (R50, universal leukocyte reference RNA as described inExample 9) and 1 μl of the Oligo dT/Random Hexamer Mix, were added towater to a final volume of 11.5 μl. The sample mix was then placed at70° C. for 10 minutes. Following the 70° C. incubation, the samples werechilled on ice, spun down, and 88.5 μl of first strand buffer mixdispensed into the reaction tube. The final first strand buffer mixproduced final concentrations of 1× first strand buffer (Invitrogen,Y00146, Carlsbad, Calif.), 0.01 mM DTT (Invitrogen, Y00147), 0.1 mM dATP(NEB, N0440S, Beverly, Mass.), 0.1 mM dGTP (NEB, N0442S), 0.1 mM dTTP(NEB, N0443S), 0.1 mM dCTP (NEB, N0441S), 2 U of reverse transcriptase(Superscript II, Invitrogen, 18064-014), and 0.18 U of RNase inhibitor(RNAGaurd Amersham Pharmacia, 27-0815-01, Piscataway, N.J.). Thereaction was incubated at 42° C. for 1 hour. After incubation the enzymewas heat inactivated at 70° C. for 15 minutes, 1 μl of RNAse H added tothe reaction tube, and incubated at 37° C. for 20 minutes.

Primer Design

Two methods were used to design primers. The first was to use thesoftware, Primer Express™ and recommendations for primer design that areprovided with the GeneAmp® 7700 Sequence Detection System supplied byApplied BioSystems (Foster City, Calif.). The second method used todesign primers was the PRIMER3 ver 0.9 program that is available fromthe Whitehead Research Institute, Cambridge, Mass. at the web sitegenome.wi.mit.edu/genome_software/other/primer3.html. The program canalso be accessed on the World Wide Web at the web sitegenome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi. Primers andTaqman/hybridization probes were designed as described below using bothprograms.

The Primer Express literature explains that primers should be designedwith a melting temperature between 58 and 60 degrees C. while the Taqmanprobes should have a melting temperature of 68 to 70 under the saltconditions of the supplied reagents. The salt concentration is fixed inthe software. Primers should be between 15 and 30 basepairs long. Theprimers should produce and amplicon in size between 50 and 150 basepairs, have a C-G content between 20% and 80%, have no more than 4identical base pairs next to one another, and no more than 2 C's and G'sin the last 5 bases of the 3′ end. The probe cannot have a G on the 5′end and the strand with the fewest G's should be used for the probe.

Primer3 has a large number of parameters. The defaults were used for allexcept for melting temperature and the optimal size of the amplicon wasset at 100 bases. One of the most critical is salt concentration as itaffects the melting temperature of the probes and primers. In order toproduce primers and probes with melting temperatures equivalent toPrimer Express, a number of primers and probes designed by PrimerExpress were examined using PRIMER3. Using a salt concentration of 50 mMthese primers had an average melting temperature of 3.7 degrees higherthan predicted by Primer Express. In order to design primers and probeswith equivalent melting temperatures as Primer Express using PRIMER3, amelting temperature of 62.7 plus/minus 1.0 degree was used in PRIMER3for primers and 72.7 plus/minus 1.0 degrees for probes with a saltconcentration of 50 mM.

The C source code for Primer3 was downloaded and complied on a SunEnterprise 250 server using the GCC complier. The program was then usedfrom the command line using a input file that contained the sequence forwhich we wanted to design primers and probes along with the inputparameters as described by help files that accompany the software. Usingscripting it was possible to input a number of sequences andautomatically generate a number of possible probes and primers.

Primers for β-Actin (Beta Actin, Genbank Locus: NM_(—)001101) and β-GUS:glucuronidase, beta, (GUSB, Genbank Locus: NM_(—)000181), two referencegenes, were designed using both methods and are shown here as examples:

The first step was to mask out repetitive sequences found in the mRNAsequences using RepeatMasker program that can be accessed at: the website repeatmasker.genome.washington.edu/cgi-bin/RepeatMasker (Smit, AFA& Green, P “RepeatMasker” at the web siteftp.genome.washington.edu/RM/RepeatMasker.html).

The last 500 basepairs on the last 3′ end of masked sequence was thensubmitted to PRIMER3 using the following exemplary input file:

PRIMER_SEQUENCE_ID=>ACTB Beta Actin PRIMER_EXPLAIN_FLAG=1PRIMER_MISPRIMING_LIBRARY= (SEQ ID NO: 9191)SEQUENCE=TTGGCTTGACTCAGGATTTAAAAACTGGAACGGTGAAGGTGACAGCAGTCGGTTGGACGAGCATCCCCCAAAGTTCACAATGTGGCCGAGGACTTTGATTGCACATTGTTGTTTTTTAATAGTCATTCCAAATATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCACCCCACTTCTCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGGATAGCATTGCTTTCGTGTAAATTATGTAATGCAAAATTTTTTTAATCTTCGCCTTAATCTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTCCCCCTTTTTTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGGAGGCAGCCGGGCTTACCTGTACACTGACTTGAGACCAGTTGAATAAAAGTGCACACCTTA PRIMER_PRODUCT_OPT_SIZE=100PRIMER_NUM_RETURN=100 PRIMER_MAX_END_STABILITY=9.0PRIMER_MAX_MISPRIMING=12.00 PRIMER_PAIR_MAX_MISPRIMING=24.00PRIMER_MIN_SIZE=18 PRIMER_OPT_SIZE=20 PRIMER_MAX_SIZE=32PRIMER_MIN_TM=61.7 PRIMER_OPT_TM=62.7 PRIMER_MAX_TM=63.7PRIMER_MAX_DIFF_TM=100.0 PRIMER_MIN_GC=20.0 PRIMER_MAX_GC=80.0PRIMER_SELF_ANY=8.00 PRIMER_SELF_END=3.00 PRIMER_NUM_NS_ACCEPTED=0PRIMER_MAX_POLY_X=4 PRIMER_OUTSIDE_PENALTY=0 PRIMER_GC_CLAMP=0PRIMER_SALT_CONC=50.0 PRIMER_DNA_CONC=50.0 PRIMER_LIBERAL_BASE=1PRIMER_MIN_QUALITY=0 PRIMER_MIN_END_QUALITY=0 PRIMER_QUALITY_RANGE_MIN=0PRIMER_QUALITY_RANGE_MAX=100 PRIMER_WT_TM_LT=1.0 PRIMER_WT_TM_GT=1.0PRIMER_WT_SIZE_LT=1.0 PRIMER_WT_SIZE_GT=1.0 PRIMER_WT_GC_PERCENT_LT=0.0PRIMER_WT_GC_PERCENT_GT=0.0 PRIMER_WT_COMPL_ANY=0.0PRIMER_WT_COMPL_END=0.0 PRIMER_WT_NUM_NS=0.0 PRIMER_WT_REP_SIM=0.0PRIMER_WT_SEQ_QUAL=0.0 PRIMER_WT_END_QUAL=0.0 PRIMER_WT_POS_PENALTY=0.0PRIMER_WT_END_STABILITY=0.0 PRIMER_PAIR_WT_PRODUCT_SIZE_LT=0.05PRIMER_PAIR_WT_PRODUCT_SIZE_GT=0.05 PRIMER_PAIR_WT_PRODUCT_TM_LT=0.0PRIMER_PAIR_WT_PRODUCT_TM_GT=0.0 PRIMER_PAIR_WT_DIFF_TM=0.0PRIMER_PAIR_WT_COMPL_ANY=0.0 PRIMER_PAIR_WT_COMPL_END=0.0PRIMER_PAIR_WT_REP_SIM=0.0 PRIMER_PAIR_WT_PR_PENALTY=1.0PRIMER_PAIR_WT_IO_PENALTY=0.0 PRIMER_INTERNAL_OLIGO_MIN_SIZE=18PRIMER_INTERNAL_OLIGO_OPT_SIZE=20 PRIMER_INTERNAL_OLIGO_MAX_SIZE=35PRIMER_INTERNAL_OLIGO_MIN_TM=71.7 PRIMER_INTERNAL_OLIGO_OPT_TM=72.7PRIMER_INTERNAL_OLIGO_MAX_TM=73.7 PRIMER_INTERNAL_OLIGO_MIN_GC=20.0PRIMER_INTERNAL_OLIGO_MAX_GC=80.0 PRIMER_INTERNAL_OLIGO_SELF_ANY=12.00PRIMER_INTERNAL_OLIGO_SELF_END=12.00 PRIMER_INTERNAL_OLIGO_NUM_NS=0PRIMER_INTERNAL_OLIGO_MAX_POLY_X=5 PRIMER_INTERNAL_OLIGO_MISHYB_LIBRARY=PRIMER_INTERNAL_OLIGO_MAX_MISYB=12.00PRIMER_INTERNAL_OLIGO_MIN_QUALITY=0 PRIMER_INTERNAL_OLIGO_SALT_CONC=50.0PRIMER_INTERNAL—OLIGO_DNA_CONC=50.0 PRIMER_IO_WT_TM_LT=1.0PRIMER_IO_WT_TM_GT=1.0 PRIMER_IO_WT_SIZE_LT=1.0 PRIMER_IO_WT_SIZE_GT=1.0PRIMER_IO_WT_GC_PERCENT_LT=0.0 PRIMER—IO_WT_GC_PERCENT_GT=0.0PRIMER_IO_WT_COMPL_ANY=0.0 PRIMER_IO_WT_NUM_NS=0.0PRIMER_IO_WT_REP_SIM=0.0 PRIMER_IO_WT_SEQ_QUAL=0.0PRIMER_TASK=pick_pcr_primers_and_hyb_probePRIMER_PRODUCT_SIZE_RANGE=50-150 PRIMER_FIRST_BASE_INDEX=1PRIMER_PICK_ANYWAY=1 = PRIMER_SEQUENCE_ID=>GUSB PRIMER_EXPLAIN_FLAG=1PRIMER_MISPRIMING_LIBRARY= (SEQ ID NO: 9192)SEQUENCE=GAAGAGTACCAGAAAAGTCTGCTAGAGCAGTACCATCTGGGTCTGGATCAAAAACGCAGAAAATATGTGGTTGGAGAGCTCATTTGGAATTTTGCCGATTTCATGACTGAACAGTCACCGACGAGAGTGCTGGGGAATAAAAAGGGGATCTTCACTCGGCAGAGACAACCAAAAAGTGCAGCGTTCCTTTTGCGAGAGAGATACTGGAAGATTGCCAATGAAACCAGGTATCCCCACTCAGTAGCCAAGTCACAATGTTTGGAAAACAGCCCGTTTACTTGAGCAAGACTGATACCACCTGCGTGTCCCTTCCTCCCCGAGTCAGGGCGACTTCCACAGCAGCAGAACAAGTGCCTCCTGGACTGTTCACGGCAGACCAGAACGTTTCTGGCCTGGGTTTTGTGGTCATCTATTCTAGCAGGGAACACTAAAGGTGGAAATAAAAGATTTTCTATTATGGAAATAAAGAGT TGGCATGAAAGTCGCTACTGPRIMER_PRODUCT_OPT_SIZE=100 PRIMER_NUM_RETURN=100PRIMER_MAX_END_STABILITY=9.0 PRIMER_MAX_MISPRIMING=12.00PRIMER_PAIR_MAX_MISPRIMING=24.00 PRIMER_MIN_SIZE=18 PRIMER_OPT_SIZE=20PRIMER_MAX_SIZE=32 PRIMER_MIN_TM=61.7 PRIMER_OPT_TM=62.7PRIMER_MAX_TM=63.7 PRIMER_MAX_DIFF_TM=100.0 PRIMER_MIN_GC=20.0PRIMER_MAX_GC=80.0 PRIMER_SELF_ANY=8.00 PRIMER_SELF_END=3.00PRIMER_NUM_NS_ACCEPTED=0 PRIMER_MAX_POLY_X=4 PRIMER_OUTSIDE_PENALTY=0PRIMER_GC_CLAMP=0 PRIMER_SALT_CONC=50.0 PRIMER_DNA_CONC=50.0PRIMER_LIBERAL_BASE=1 PRIMER_MIN_QUALITY=0 PRIMER_MIN_END_QUALITY=0PRIMER_QUALITY_RANGE_MIN=0 PRIMER_QUALITY_RANGE_MAX=100PRIMER_WT_TM_LT=1.0 PRIMER_WT_TM_GT=1.0 PRIMER_WT_SIZE_LT=1.0PRIMER_WT_SIZE—GT=1.0 PRIMER_WT_GC_PERCENT_LT=0.0PRIMER_WT_GC_PERCENT_GT=0.0 PRIMER_WT_COMPL_ANY=0.0PRIMER_WT_COMPL_END=0.0 PRIMER_WT_NUM_NS=0.0 PRIMER_WT_REP_SIM=0.0PRIMER_WT_SEQ_QUAL=0.0 PRIMER_WT_END_QUAL=0.0 PRIMER_WT_POS_PENALTY=0.0PRIMER_WT_END_STABILITY=0.0 PRIMER_PAIR_WT_PRODUCT_SIZE_LT=0.05PRIMER_PAIR_WT_PRODUCT_SIZE_GT=0.05 PRIMER_PAIR_WT_PRODUCT_TM_LT=0.0PRIMER_PAIR_WT_PRODUCT_TM_GT=0.0 PRIMER_PAIR_WT_DIFF_TM=0.0PRIMER_PAIR_WT_COMPL_ANY=0.0 PRIMER_PAIR_WT_COMPL_END=0.0PRIMER_PAIR_WT_REP_SIM=0.0 PRIMER_PAIR_WT_PR_PENALTY=1.0PRIMER_PAIR_WT_IO_PENALTY=0.0 PRIMER_INTERNAL_OLIGO_MIN_SIZE=18PRIMER_INTERNAL_OLIGO_OPT_SIZE=20 PRIMER_INTERNAL_OLIGO_MAX_SIZE=35PRIMER_INTERNAL_OLIGO_MIN_TM=71.1 PRIMER_INTERNAL_OLIGO_OPT_TM=72.7PRIMER_INTERNAL_OLIGO_MAX_TM=73.7 PRIMER_INTERNAL_OLIGO_MIN_GC=20.0PRIMER_INTERNAL_OLIGO_MAX_GC=80.0 PRIMER_INTERNAL_OLIGO_SELF_ANY=12.00PRIMER_INTERNAL_OLIGO_SELF_END=12.00 PRIMER_INTERNAL_OLIGO_NUM_NS=0PRIMER_INTERNAL_OLIGO_MAX_POLY_X=5 PRIMER_INTERNAL_OLIGO_MISHYB_LIBRARY=PRIMER_INTERNAL_OLIGO_MAX_MISHYB=12.00PRIMER_INTERNAL_OLIGO_MIN_QUALITY=0 PRIMER_INTERNAL_OLIGO_SALT_CONC=50.0PRIMER_INTERNAL_OLIGO_DNA_CONC=50.0 PRIMER_IO_WT_TM_LT=1.0PRIMER_IO_WT_TM_GT=1.0 PRIMER_IO_WT_SIZE_LT=1.0 PRIMER_IO_WT_SIZE_GT=1.0PRIMER_IO_WT_GC_PERCENT_LT=0.0 PRIMER_IO_WT_GC_PERCENT_GT=0.0PRIMER_IO_WT_COMPL_ANY=0.0 PRIMER_IO_WT_NUM_NS=0.0PRIMER_IO_WT_REP_SIM=0.0 PRIMER_IO_WT_SEQ_QUAL=0.0PRIMER_TASK=pick_pcr_primers_and_hyb_probePRIMER_PRODUCT_SIZE_RANGE=50-150 PRIMER_FIRST_BASE_INDEX=1PRIMER_PICK_ANYWAY=1 =

After running PRIMERS, 100 sets of primers and probes were generated forACTB and GUSB. From this set, nested primers were chosen based onwhether both left primers could be paired with both right primers and asingle Taqman probe could be used on an insert of the correct size. Withmore experience we have decided not use the mix and match approach toprimer selection and just use several of the top pairs of predictedprimers.

For ACTB this turned out to be:

Forward 75 CACAATGTGGCCGAGGACTT, (SEQ ID NO: 9174) Forward 80TGTGGCCGAGGACTTTGATT, (SEQ ID NO: 9175) Reverse 178TGGCTTTTAGGATGGCAAGG, (SEQ ID NO: 9176) and Reverse 168GGGGGCTTAGTTTGCTTCCT. (SEQ ID NO: 9177)

Upon testing, the F75 and R178 pair worked best.

For GUSB the following primers were chosen:

Forward 59 AAGTGCAGCGTTCCTTTTGC, (SEQ ID NO: 9178) Forward 65AGCGTTCCTTTTGCGAGAGA, (SEQ ID NO: 9179) Reverse 158CGGGCTGTTTTCCAAACATT, (SEQ ID NO: 9180) and Reverse 197GAAGGGACACGCAGGTGGTA. (SEQ ID NO: 9181)

No combination of these GUSB pairs worked well.

In addition to the primer pairs above, Primer Express predicted thefollowing primers for GUSB: Forward 178 TACCACCTGCGTGTCCCTTC (SEQ ID NO:9182) and Reverse 242 GAGGCACTTGTTCTGCTGCTG (SEQ ID NO: 9183). This pairof primers worked to amplify the GUSB mRNA.

The parameters used to predict these primers in Primer Express were:Primer Tm: min 58, Max=60, opt 59, max difference=2 degreesPrimer GC: min=20% Max=80% no 3′ G/C clampPrimer: Length: min=9 max=40 opt=20

Amplicon: min Tm=0 max Tm=85

-   -   min=50 by max=150 bμ        Probe: Tm 10 degrees >primers, do not begin with a G on 5′ end        Other: max base pair repeat=3    -   max number of ambiguous residues=0    -   secondary structure: max consec by=4, max total by=8    -   Uniqueness: max consec match=9        -   max % match=75        -   max 3′ consecutive match=7

Granzyme B is an important marker of CMV infection and transplantrejection.

Tables 13 A and B.

For Granzyme B the following sequence (NM_(—)004131) was used as inputfor Primer3:

(SEQ ID NO: 9184) GGGGACTCTGGAGGCCCTCTTGTGTGTAACAAGGTGGCCCAGGGCATTGTCTCCTATGGACGAAACAATGGCATGCCTCCACGAGCCTGCACCAAAGTCTCAAGCTTTGTACACTGGATAAAGAAAACCATGAAACGCTACTAACTACAGGAAGCAAACTAAGCCCCCGCTGTAATGAAACACCTTCTCTGGAGCCAAGTCCAGATTTACACTGGGAGAGGTGCCAGCAACTGAATAAATACCTCTCCCAGTGTAAATCTGGAGCCAAGTCCAGATTTAGACTGGGAGAGGTGCCAGCAACTGAATAAATACCTCTTAGCTGAGTGGFor Granzyme B the following primers were chosen for testing:

Forward 81 ACGAGCCTGCACCAAAGTCT (SEQ ID NO: 9185) Forward 63AAACAATGGCATGCCTCCAC (SEQ NO: 9186) Reverse 178 TCATTACAGCGGGGGCTTAG(SEQ ID NO: 9187) Reverse 168 GGGGGCTTAGTTTGCTTCCT (SEQ ID NO: 9188)

Testing demonstrated that F81 and R178 worked well.

Using this approach, multiple primers were designed for genes that wereshown to have expression patterns that correlated with clinical data inexamples 17 and 24. These primer pairs are shown in Tables 13B and 14Band are added to the sequence listing. Primers can be designed from anyregion of a target gene using this approach.

Primer Endpoint Testing

Primers were first tested to examine whether they would produce thecorrect size product without non-specific amplification. The standardreal-time PCR protocol was used with out the Rox and Sybr green dyes.Each primer pair was tested on cDNA made from universal mononuclearleukocyte reference RNA that was produced from 50 individuals asdescribed in Example 9 (R50).

The PCR reaction consisted of 1× RealTime PCR Buffer (Ambion, Austin,Tex.), 3 mM MgC12 (Applied BioSystems, B02953), 0.2 mM dATP (NEB), 0.2mM dTTP (NEB), 0.2 mM dCTP (NEB), 0.2 mM dGTP (NEB), 1.25 U AmpliTaqGold (Applied BioSystems, Foster City, Calif.), 0.3 μM of each primer tobe used (Sigma Genosys, The Woodlands, Tex.), 5 μl of the R50reverse-transcription reaction and water to a final volume of 19 ml

Following 40 cycles of PCR, one microliter of the product was examinedby agarose gel electrophoresis and on an Agilent Bioanalyzer, DNA1000chip (Palo Alto, Calif.). Results for 2 genes are shown in FIG. 11. Fromthe primer design and the sequence of the target gene, one can calculatethe expected size of the amplified DNA product. Only primer pairs withamplification of the desired product and minimal amplification ofcontaminants were used for real-time PCR. Primers that produced multipleproducts of different sizes are likely not specific for the gene ofinterest and may amplify multiple genes or chromosomal loci.

Primer Optimization/Efficiency

Once primers passed the end-point PCR, the primers were tested todetermine the efficiency of the reaction in a real-time PCR reaction.cDNA was synthesized from starting total RNA as described above. A setof 5 serial dilutions of the R50 reverse-transcribed cDNA (as describedabove) were made in water: 1:10, 1:20, 1:40, 1:80, and 1:160.

The Sybr Green real-time PCR reaction was performed using the Taqman PCRReagent kit (Applied BioSystems, Foster City, Calif., N808-0228). Amaster mix was made that consisted of all reagents except the primes andtemplate. The final concentration of all ingredients in the reaction was1× Taqman Buffer A (Applied BioSystems), 2 mM MgC12 (AppliedBioSystems), 2001.1M dATP (Applied BioSystems), 2001.1M dCTP (AppliedBioSystems), 2001.1M dGTP (Applied BioSystems), 4001.1M dUTP (AppliedBioSystems), 1:400,000 diluted Sybr Green dye (Molecular Probes), 1.25 UAmpliTaq Gold (Applied BioSystems). The master mix for 92 reactions wasmade to a final volume of 2112 μl. 1012 μl of PCR master mix wasdispensed into two, light-tight tubes. Each β-Actin primer F75 and R178(Genosys), was added to one tube of PCR master mix and Each 13-GUSprimer F178 and R242 (Genosys), was added to the other tube of PCRmaster mix to a final primer concentration of 300 nM, and a final volumeof 1035 μl per reaction tube. 45 μl of the β-Actin master mix wasdispensed into 23 wells, in a 96 well plate (Applied BioSystems). 45 μlof the β-GUS master mix was dispensed into 23 wells, in a 96 well plate(Applied BioSystems). 5 μl of the template dilution series was dispensedinto triplicate wells for each primer. The reaction was run on an ABI7700 Sequence Detector (Applied BioSystems).

The Sequence Detector v1.7 software was used to analyze the fluorescentsignal from each well. A threshold value was selected that allowed mostof the amplification curves to cross the threshold during the linearphase of amplification. The cycle number at which each amplificationcurve crossed the threshold (C_(T)) was recorded and the filetransferred to MS Excel for further analysis. The C_(T) values fortriplicate wells were averaged. The data were plotted as a function ofthe log₁₀ of the calculated starting concentration of RNA. The startingRNA concentration for each cDNA dilution was determined based on theoriginal amount of RNA used in the RT reaction, the dilution of the RTreaction, and the amount used (5 μl) in the real-time PCR reaction. Foreach gene, a linear regression line was plotted through all of thedilutions series points. The slope of the line was used to calculate theefficiency of the reaction for each primer set using the equation:

E=10^((−1/slope))

Using this equation (Pfaffl 2001), the efficiency for these β-actinprimers is 2.28 and the efficiency for these β-GUS primers is 2.14 (FIG.12). This efficiency was used when comparing the expression levels amongmultiple genes and multiple samples. This same method was used tocalculate reaction efficiency for primer pairs for each gene we studied.

Assay and Results

Once primers were designed and tested and efficiency analysis wascompleted, primers were used examine expression of a single gene amongmany clinical samples. The basic design was to examine expression ofboth the experimental gene and a reference gene in each sample and, atthe same time, in a control sample. The control sample we used was theuniversal mononuclear leukocyte reference RNA described in example 9(R50).

In this example, three patient samples from patients with known CMVinfection were compared to three patient samples from patients with nodiagnosis of CMV infection based on standard diagnostic algorithms foractive CMV infection (including viral PCR assays, serologies, cultureand other tests). cDNA was made from all six RNA samples and the R50control as described above. The cDNA was diluted 1:10 in water and 5 μlof this dilution was used in the 50 μl PCR reaction. Each 96-well plateconsisted of 32 reactions, each done in triplicate. There were 17templates and 3 primer sets. The three primer sets were β-GUS, β-Actin,and Granzyme B. The β-GUS and β-Actin primers are shown above and theGranzyme B primers were:

F81: ACGAGCCTGCACCAAAGT, SEQ ID NO: 9189 R178: TCATTACAGCGGGGGCTT,SEQ ID NO: 9190

Each of the three primer sets was used to measure template levels in 8templates: the six experimental samples, R50, and water (no-templatecontrol). The β-GUS primers were also used to measure template levels aset of 8 templates identical except for the absence of the reversetranscriptase enzyme in the cDNA synthesis reaction (−RT). The real-timePCR reactions were performed as described above in “primeroptimization/efficiency”.

The β-GUS amplification with +RT and −RT cDNA synthesis reactiontemplates were compared to measure the amount of genomic DNAcontamination of the patient RNA sample (FIG. 10A). The only source ofamplifiable material in the −RT cDNA synthesis reaction is contaminatinggenomic DNA. Separation by at least four C_(T) between the −RT and +RTfor each sample was required to consider the sample useful for analysisof RNA levels. Since a C_(T) decrease of one is a two-fold increase intemplate, a difference of four C_(T) would indicate that genomic DNAcontamination level in the +RT samples was 6.25% of the total signal.Since we used these reactions to measure 30% or greater differences, a6% contamination would not change the result.

For samples with sufficiently low genomic DNA contamination the datawere used to identify differences in gene expression by measuring RNAlevels. C_(T) values from the triplicate wells for each reaction wereaveraged and the coefficient of variation (CV) determined Samples withhigh CV (>2%) were examined and outlier reaction wells were discardedfrom further analysis. The average of the wells for each sample wastaken as the C_(T) value for each sample. For each gene, the ΔCT was theR50 control C_(T) minus the sample C_(T). The equation below was thenused to identify an expression ratio compared to a reference gene(β-Actin) and control sample (R50) for Granzyme B expression in eachexperimental sample (Pfaffl, M. W. 2001). E is the amplificationefficiency determined above.

${ratio} = \frac{\left( E_{target} \right)^{\Delta \; C_{T}{{target}{({{control}\text{-}{sample}})}}}}{\left( E_{ref} \right)^{\Delta \; C_{T}{{ref}{({{control}\text{-}{sample}})}}}}$

The complete experiment was performed in duplicate and the average ofthe two ratios taken for each gene. When β-Actin was used as thereference gene, the data show that Granzyme B is expressed at 25-foldhigher levels in mononuclear cell RNA from patients with CMV than frompatients without CMV (FIG. 10B). In this graph, each circle represents apatient sample and the black bars are the average of the three samplesin each category.

Example 26 Correlation and Classification Analysis

After generation and processing of expression data sets from microarraysas described in Example 23, a log ratio value is used for mostsubsequent analysis. This is the logarithm of the expression ratio foreach gene between sample and universal reference. The processingalgorithm assigns a number of flags to data that are of low signal tonoise or are in some other way of uncertain quality. Correlationanalysis can proceed with all the data (including the flagged data) orcan be done on filtered data sets where the flagged data is removed fromthe set. Filtered data should have less variability and may result inmore significant results. Flagged data contains all informationavailable and may allow discovery of genes that are missed with thefiltered data set.

In addition to expression data, clinical data are included in theanalysis. Continuous variables, such as the ejection fraction of theheart measured by echocardiography or the white blood cell count can beused for correlation analysis. In some cases, it may be desirable totake the logarithm of the values before analysis. These variables can beincluded in an analysis along with gene expression values, in which casethey are treated as another “gene”. Sets of markers can be discoveredthat work to diagnose a patient condition and these can include bothgenes and clinical parameters. Categorical variables such as male orfemale can also be used as variables for correlation analysis. Forexample, the sex of a patient may be an important splitter for aclassification tree.

Clinical data are used as supervising vectors for the significance orclassification analysis. In this case, clinical data associated with thesamples are used to divide samples in to clinically meaningfuldiagnostic categories for correlation or classification analsysis. Forexample, pathologic specimens from kidney biopsies can be used to dividelupus patients into groups with and without kidney disease. A third ormore categories can also be included (for example “unknown” or “notreported”). After generation of expression data and definition of usingsupervising vectors, correlation, significance and classificationanalysis is used to determine which set of genes are most appropriatefor diagnosis and classification of patients and patient samples.

Significance Analysis for Microarrays (SAM)

Significance analysis for microarrays (SAM) (Tusher 2001) is a methodthrough which genes with a correlation between their expression valuesand the response vector are statistically discovered and assigned astatistical significance. The ratio of false significant to significantgenes is the False Discovery Rate (FDR). This means that for eachthreshold there are a set of genes which are called significant, and theFDR gives a confidence level for this claim. If a gene is calleddifferentially expressed between 2 classes by SAM, with a FDR of 5%,there is a 95% chance that the gene is actually differentially expressedbetween the classes. SAM takes into account the variability and largenumber of variables of microarrays. SAM will identify genes that aremost globally differentially expressed between the classes. Thus,important genes for identifying and classifying outlier samples orpatients may not be identified by SAM.

After generation of data from patient samples and definition ofcategories using clinical data as supervising vectors, SAM is used todetect genes that are likely to be differentially expressed between thegroupings. Those genes with the highest significance can be validated byreal-time PCR (Example 25) or can be used to build a classificationalgorithm as described here.

Classification

Supervised harvesting of expression trees (Hastie et al. 2001)identifies genes or clusters that best distinguish one class from allthe others on the data set. The method is used to identify thegenes/clusters that can best separate one class versus all the othersfor datasets that include two or more classes from each other. Thisalgorithm can be used to identify genes that are used to create adiagnostic algorithm. Genes that are identified can be used to build aclassification tree with algorithms such as CART.

CART is a decision tree classification algorithm (Breiman 1984). Fromgene expression and or other data, CART can develop a decision tree forthe classification of samples. Each node on the decision tree involves aquery about the expression level of one or more genes or variables.Samples that are above the threshold go down one branch of the decisiontree and samples that are not go down the other branch. Genes fromexpression data sets can be selected for classification building usingCART by significant differential expression in SAM analysis (or othersignificance test), identification by supervised tree-harvestinganalysis, high fold change between sample groups, or known relevance toclassification of the target diseases. In addition, clinical data canalso be used as variables for CART that are of know importance to theclinical question or are found to be significant predictors bymultivariate analysis or some other technique. CART identifiessurrogates for each splitter (genes that are the next best substitutefor a useful gene in classification). Analysis is performed in CART byweighting misclassification costs to optimize desired performance of theassay. For example, it may be most important the sensitivity of a testfor a given diagnosis be near 100% while specificity is less important.

Once a set of genes and expression criteria for those genes have beenestablished for classification, cross validation is done. There are manyapproaches, including a 10 fold cross validation analysis in which 10%of the training samples are left out of the analysis and theclassification algorithm is built with the remaining 90%. The 10% arethen used as a test set for the algorithm. The process is repeated 10times with 10% of the samples being left out as a test set each time.Through this analysis, one can derive a cross validation error whichhelps estimate the robustness of the algorithm for use on prospective(test) samples. When a gene set is established for a diagnosis with alow cross validation error, this set of genes is tested using samplesthat were not included in the initial analysis (test samples). Thesesamples may be taken from archives generated during the clinical study.Alternatively, a new prospective clinical study can be initiated, wheresamples are obtained and the gene set is used to predict patientdiagnoses.

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LENGTHY TABLES The patent application contains a lengthy table section.A copy of the table is available in electronic form from the USPTO website(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20120010096A1).An electronic copy of the table will also be available from the USPTOupon request and payment of the fee set forth in 37 CFR 1.19(b)(3).

1. A method of diagnosing or monitoring cardiac transplant rejection ina patient, comprising detecting the expression level of a nucleic acidin said patient to diagnose or monitor cardiac transplant rejection insaid patient, wherein said nucleic acid comprises the nucleotidesequence SEQ ID NO:
 1956. 2. The method of claim 1 wherein saidexpression level is detected by measuring the RNA level expressed bysaid nucleic acid.
 3. The method of claim 2, further comprisingisolating RNA from said patient prior to detecting said RNA levelexpressed by said nucleic acid.
 4. The method of claim 2 wherein saidRNA level is detected by PCR.
 5. The method of claim 2 wherein said RNAlevel is detected by hybridization.
 6. The method of claim 2 whereinsaid RNA level is detected by hybridization to an oligonucleotide. 7.The method of claim 6 wherein said oligonucleotide comprises DNA, RNA,cDNA, PNA, genomic DNA, or synthetic oligonucleotides.